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1.Data analyst in B2B Marketing : In B2B marketing data analysts will spend most of their time in analysing the data that includes the past trends,curren conditions etc.
2.Data Insights and Analyst : Hear he/she will be managing a team.Have keen eye for the detail and the quality
3.Analytics Manager : Here the person will build and manage actionable sales analytics solutions.
4.Chief Data Scientist : he has the responsibility to define the new product ideas with predictive and goal-oriented capabilities to market.Chief Data Scientist has responsibility for leading the research, development, and delivery of big data-based solutions
5.Associate Data Scientist : This person should be able to manage conformity to established procedures and processes.He will be acting as close driving agent with stakeholders.
6.Senior Analytics Manager : They will be developing predictive models in customer analytics.Track all scorecards- performances and highlight to management in case of any breaches/actions required.
7.Sales Planning Analyst : This position is responsible to support the Global Sales Organization .which helps in assisting the development of strategic plans and business processes by using Data science and Business Intelligence technologies.which is also focused towards the implementation of effective sales strategies to meet company objectives.
8.Data Engineer : this person will define, deliver, and support the data solutions. And they are also responsible for the setup, configuration, development, and ongoing operations of the company.
9.Product Manager :They will lead end-to-end roadmap of companies academic and delivery products in sync with business objectives.And also will manage all aspects of the product lifecycle, including problem definition, customer needs, requirement or usecase definition
10. General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data.
1. Machine learning engineer - there is a lot of overlap between machine learning engineer and data scientist at some companies this title just means data scientist who has specialised in machine learning.Aadhar companies machine learning engineer is more of a software engineering role that involves taking data scientists analysis and turning it into deployable software. Aldo 10 specifix query, virtually all machines learning engineer positionswill require at least Tata thanks programming skills and a pretty advanced knowledge about machine learning techniques.
2. Quantitative analyst - quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
3. Data warehouse architect- essentially this is a speciality of field winding data engineering for 4 years would like to be in charge of companies data storage systems. SQL skills are definitely going to be important follow like this, also need a solid command of other technical skills that will vary based on the employers tech stack.
4. Business intelligence analyst- Business analyst is essentially a data analyst who is focused on the analysing market and business trends. This position sometimes requires familiarity with software based data analysis tools but many data science skills are also crucial for business intelligence analyst position and many of these positions will also require python or r programming skills.
5. Statistician- statistician is what data scientist where called before turn data scientist existed. Required skills can vary quite a bit bi from job to job but all of them will require a solid understanding of probability and statistics.
6. Business analyst- business analyst is operator generic job title that's applied to a wide variety of rolls but Hindi broadest terms ab business analyst helps companies answer questions and solve problems.
7. System analyst- system analyst are often task with current flowing organisational problems and then planning and overseeing the charges for new systems required to solve those problems.
8. Marketing analyst- marketing analyst look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. in the digital age,these analyst have access to increasing Lee large amounts of data, particularly at companies that sell digital products, and while there are variety of software solutions like Google earth that can allow for decent analysis without programming skills and applicant with data science and statistics jobs is likely to have a leg up on many other applicants if they also have sufficient domain knowledge Hindi area of marketing.
9. Operation analyst- operation analysts are typically task with examining and streamlining a business internal operations. Specific duties and salaries can vary widely, and not all operations analysts positions will make use data skills coma but in many cases, being able to clean analyse, and visualise data will be important in determining what company systems are working smoothly and what areas might need improvement.
Data science is improving and changing to a vast extent over the years .its has a lot of scope in current and future in companies, now companies hiring alot of data scientists. It has considered as one of the top professional careers India. The following are the skills required to become data scientists 1. Programming language 2. Analytical knowledge 3. Communication skills 4. Technical skills 5. Statistical knowledge. *The following are the jobs available for the data science 1.Business intelligent analyst 2.data mining engineer 3.data architect 4.data scientists 5.senior data scientists 6.database system manager Explanation: 1.business intelligent analyst: It includes the technology, applications and practices for collection,integration, analysis and presentation of business information. 2.data mining engineer: These jobs develop set processes for data mining,and data modeling, and data production. 3. Data architect: A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture. 4.data scientists They are the big data wranglers,gathering and analyzing large sets of structures and unstructured data.
1. Business intelligence:Here BI developers design and develop strategies to assist business users in quickly finding the information that is needed in making better business decisions. Companies: DollarShave Club, Discover, and Liberty Mutual.
2. Data Architect:Here he/she has to ensure that data solutions are built for performance and design analytics applications for multiple platforms. Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Machine learning scientists: They research new data approach and algorithms Companies: Apple, Tinder
4. Infrastructure Architect: According to Techopedia an enterprise architect works closely with stakeholders, including management and subject matter experts (SME),to develop a view of an organization’s strategy, information, processes and IT assets. Companies: Cisco, Boeing, Lockheed Martin, Microsoft
5. Data scientists:He/she has to find, clean, and organize data for companies. Companies: Facebook, Twitter
6. Data engineers: they have to perform batch processing or real time processing on gathered and stored data. Companies: Spotify, Verizon, General Motors, Shutterfly
1) Job Openings in Data science sector in 2019 - There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual. Forecasting annual growth in data science professionals in 2018: Compared to the numbers in 2017, last year had an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared t0 2017.
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecome.
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability.
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities.
7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers.
Some of the Data Science Jobs available in the current market are Data Analyst, Data Scientist and Data Engineer.
Other then these posts there are different job titles in Data Science such as Machine Learning Engineer, Quantitative Analyst, Data Warehouse Architect, Business Intelligence Analyst, Statistician Etc.
The Big Three are: 1. Data Analyst:- A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2. Data Scientist:- A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
3. Data Engineer:- A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
There are two different markets for data science and analytics jobs. Across the ecosystem, we see two broad families: analytics-enabled jobs and data science jobs.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data. Each of these markets requires its own strategy: sourcing from small pools of experienced data scientists and analysts for one, and employee development for the other.
1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
“A company like this is a great place for an aspiring data scientist to learn the ropes.”
Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.
3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
“Machine Learning Engineers often focus more on producing great data-driven products than they do answering operational questions for a company.”
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
“Some of the most important ‘data generalist’ skills are familiarity with tools designed for ‘big data,’ and experience with messy, ‘real-life’ datasets.”
Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning
1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual. 4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities 7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers Some of the highlights are as follows:- ✓While, it is difficult to ascertain the exact number of open analytics job openings; according to our estimates, close to 97,000 positions related to analytics & data science are currently available to be filled in India. ✓This is almost 45% jump in the open job requirements, compared to same time a year back. ✓Compared to worldwide estimates, India contributes 6% of open job openings currently. Growth in the number of data science jobs globally was much higher than India. ✓Last year India contributed 10% of worldwide open job requirements which has decreased to 6% this year, even though there have been an overall growth in numbers. ✓10 leading organisations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte. ✓Almost 97% of analytics jobs advertised in India are of full-time basis. Just 3% form the part-time, internship or contractual jobs.
4 Types of Data Science Jobs 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
“A company like this is a great place for an aspiring data scientist to learn the ropes.”
Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.
3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
“Machine Learning Engineers often focus more on producing great data-driven products than they do answering operational questions for a company.”
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
“Some of the most important ‘data generalist’ skills are familiarity with tools designed for ‘big data,’ and experience with messy, ‘real-life’ datasets.”
Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.
Data science job is used to analyse data for actionable insights and it describes jobs that are drastically different from one another in current market structure . In more general terms a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. This process requires persistence, statistics, and software engineering skills & it also includes the skills that are also necessary for understanding biases in the data, and for debugging, logging output from code. It consist of a specific tasks that includes the following: 1. Identifying the data analytics problems that offer the greatest opportunities to the organization. 2. Determining the correct data sets and variables. 3. Collecting large sets of structured and unstructured data from Disparate sources. 4. Cleaning and validating the data to ensure accuracy, completeness, and uniformity. 5. Devising and applying models and algorithms to mine the stores of big data. 6. Analysing the data to identify patterns and trends. 7. Communicating findings to stakeholders using visualization and other means. 8. Interpreting the data to discover solutions and opportunities.
A data scientist is someone who makes value out of data.. Data scientist duties typically include creating various machine learning based tools or processes within the company such as recommendation engines or automated lead scoring systems.
4 types of data science jobs 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards.
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward.
3. The Machine Learning Engineer There are a number of companies for whom their data is their product. In this case, the data analysis or machine learning going on can be pretty intense.
4. The data science generalist: A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company.
1. IT Systems Analyst Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.
2. Healthcare Data Analyst Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.
The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs. Plus, with a rise in regulations and restrictions on how that data can be stored, retrieved, and processed, demand for proficient data analysts is on the rise as well.
3. Operations Analyst Operations analysts are usually found internally at large companies, but may also work as consultants.
Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military and can make upwards of $75,000 annually. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.
4. Data Scientist Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries.
Data Science is a field that uses scientific methods, processes and systems to extract knowledge and insights from various structural and unstructured data. Data Science is considered as an entirely new career field. Data Science is related to Data Mining and Big Data. Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Skills that are required by a person working in that Data Science field are as follows:- 1) Programming Language Skills i.e. Python & SQL 2) Data Visualization or Analytical Skills 3) Statistical Knowledge
The Data Science Jobs Available in the Current Market are as follows:- 1) Data Analyst 2) Data Scientist 3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data. A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills. Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
The Various Other Posts Available are:- Machine Learning Scientists, Business Intelligent Analyst, Statistician, Quantitative Analyst & Data Warehouse Architect.
Data Science is a field that uses scientific methods, processes and systems to extract knowledge and insights from various structural and unstructured data. Data Science is considered as an entirely new career field. Data Science is related to Data Mining and Big Data. Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Skills that are required by a person working in that Data Science field are as follows:- 1) Programming Language Skills i.e. Python & SQL 2) Data Visualization or Analytical Skills 3) Statistical Knowledge
The Data Science Jobs Available in the Current Market are as follows:- 1) Data Analyst 2) Data Scientist 3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data. A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills. Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
The Various Other Posts Available are:- Machine Learning Scientists, Business Intelligent Analyst, Statistician, Quantitative Analyst & Data Warehouse Architect.
1) OPERATION ANALYST Operation analyst work as a consultant and even work internally in large organisation.His main focus is on internal processes of a business like internal reporting system, product manufacturing and distribution, and the general streamlining of business operations. They have a technical knowledge of the systems they are working with and it’s more important for professionals. 2) DATA ANALYST Data scientists collect and analyze data and communicate actionable insights.They are able to understand data from a more informed perspective. These positions require a strong knowledge of data analytics like software tools, programming languages like Python or R, and data visualization skills etc 3) DATA ENGINEER Data engineers focus more on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.As they are the high-level data analytics professionals they need to upgrade a database infrastructure for faster queries. 4) QUANTITATIVE ANALYST A quantitative analyst is another data science job, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems. They may also venture out on their own creating trading models to predict the prices of stocks, commodities, exchange rates, etc. 5) DATA ANALYST CONSULTANT Data analytics consultant is to deliver insights to a company to help their business. The difference between a consultant and data analyst is that a consultant may work for different companies in a shorter period of time and also for more than 1 company at a time. 6) TRANSPORTATION LOGISTICS SPECIALIST A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies like Amazon, UPS, airlinea etc. They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.They also need to identify the most efficient paths for products and services to be delivered. 7) PROJECT MANAGER Project managers is a person who use analytics tools to keep track of a teams progress,track their efficiency, and increase productivity by changing processes.Project managers need at least a working understanding of data analytics, and often more.These positions are usually found internally at large corporations and in management consulting too.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. 2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills. 3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathem4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.atics, statistics, or physics background and is hoping to continue down a more academic path. 5.Data Architect:Here he/she has to ensure that data solutions are built for performance and design analytics applications for multiple platforms. Companies: IBM, eBay, AAA Club Alliance, T-Mobile
Data scientists : data scientists responible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Data Science Career Opportunities : A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. Therefore it comes as no surprise that Data Scientists are coveted professionals in the Big Data Analytics and IT industry.
Data Scientist Salary Trends: A report by Glassdoor shows that Data scientists lead the pack for the best jobs in America. The report goes on to say that the median salary for a Data Scientist is an impressive $91,470 in the US and ₹622,162 and there are over 2300 job openings posted on the site In India the trend is no different; as of May 2019, the median salary for a Data Scientist role is Rs. 622,162 according to Payscale.com.
Data Scientist Job Roles: A Data Scientist dons many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Some of the prominent Data Scientist job titles are: • Data Scientist • Data Architect • Data Administrator • Data Analyst • Business Analys
Data science is considered as a new career field in various platforms like Industry, tools and operations etc companies are bringing more data oriented personnel and for good reason As a data scientist they are prevalent in Knowing how shifting will be done in near feature The current trends which ensured to except data science are: 1)Automation: This is a huge one especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system. 2) Data Empowerment Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties. 3)Ethics and Influence: The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers 4)Blockchain App Development: Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well namely because of the implications blockchain has to a great many industries.
Each of these markets requires its own strategy sourcing pools of experienced data scientists and analysts for one, and employee development for the other Thus data science jobs available in various current market has helped in emerging discipline at various levels of academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
Data Science-Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. India is expected to see 1.5 lakh new openings in the emerging and in-demand field of data science in 2020 -- an increase of around 62% compared to 2019.
Data Analyst- A data analyst collects and stores data on sales numbers, market research, logistics, linguistics, or other behaviors. They bring technical expertise to ensure the quality and accuracy of that data, then process, design and present it in ways to help people, businesses, and organizations make better decisions.
Data Engineers- Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages. But data engineers also need communication skills to work across departments to understand what business leaders want to gain from the company’s large datasets.
Business Intelligence Developer - The general role of a business intelligence developer is to create and manage an organization’s business intelligence and analytics solutions, allowing companies to make better and more informed decisions. A business intelligence developer is responsible for designing and developing BI solutions, overseeing the implementation of the BI systems and also the ongoing maintenance of those solutions. They will create and complete data request queries and, through visualization and reports, present the information.
Quantum computing- Quantum computers are known to perform complex calculations in supersonic speed, completing the computational tasks in a few seconds. The quantum bits (QuBits) can store a large volume of data and run complex computations in split seconds. Companies like Intel, Google, and IBM, are going to be the pioneers of this industry, but they are yet in their humble beginnings.
Automated Machine Learning- AutomML is growing in prominence. It is an automation layer on top of machine learning, boosting the development efficiency and overall productivity of data scientists. The data science tasks like data preparation, choosing data sources, and feature selection are repetitive and monotonous. AutoML looks into automating these tasks. AutoML can also perform quality modelling and fine-tuning of artificial intelligence (AI) and machine learning algorithms. Giants like Facebook and Google are already using AutoML for their internal data science processes.
Data science is the study of data.It involves developing methods.It is a concept to unify statistics data analysis. Data science job involves the following steps or process. 1.Business problem - Firstly need to understand the problems which need to be solved. 2.Data acquisition - After identifying the problem.Gathers information through web series,logs,databases, API's, online respositories. 3.Data preparation - This step involves data cleaning And transformation.It is time consuming as it is handling many complex scenario. 4.Exploratory data analysis- It defines Nd refines selection of feature variables that will be used in model development. 5.Data modeling - Here we can apply diverse machinery technique like KNN, Decision tree, Naive Bayes to the data.To identify the best suitable the business requirements and test them to select best performing. 6. Visualization and communication - After going through the above procedure need to prepare reports Nd recommendations which helps to present and commucates . 7.Deploys and maintain - Test the selected model in pre-marketing before using it in production system and uses the prepared report to maintain performance.
Data science involves various disciplines, including statistics, data analysis, machine learning, and computer science. Different companies have their different roles some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. Data scientist is often used as a blanket title to describe jobs that are drastically different. Let’s looks at four kind of data science jobs.
The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.once if this can be handled on day today basis company will have great environment to apply the new things.
The Data Engineer Some have to handle lot of data and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward.they also look for analyst Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
The Machine Learning There are a number of companies for whom their data is their product.In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc. these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.
Different companies are seeking different skillsets, expertise, and experience levels. As you look for your ideal data scientist job, make sure to look closely at the job descriptions, to find the role and company that best match your skills and experience Each of these types of data scientists have different skills that matter most.
1 Statistician: the field of statistics has always been about number crunching.a strong statistical base qualifies you to explore your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, analysis of variance, data visualization are some of the skills possessed by statisticians 2 Data scientist as mathematician: Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. 3 Data engineers: These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. 4 Machine learning scientists:machine learning scientists are the programmers who develop machines and systems that can learn and apply knowledge without specific direction.artificial intelligence is the goal of a machine learning engineer. 5 Digital analytic consultant: This is a very popular position and a number of organizations – ranging from Fortune 500 to small non – for – profits – seek digital analytics talent. It is a common misconception that a digital analytic professional only needs technical talent. In addition, one also needs to be sound in business and marketing skills to be successful. 6 Quality Analyst: Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. These are the some data scientists or the data engineer jobs available in the current market.They identify the problems that arises in the organisation and also determine the correct data sets and variables.they also collect the structured and unstructured data from disparate sources.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways.
Data science isn't exactly IT. It requires fewer number of people, but the expected productivity has to be very good. Whether we're making a ML product or solving business problems such as price points at which I should sale, it requires a few people to use their brain on varied work than a set of redundant work that IT normally does. Think of data science as the most advanced and coding and mathematics heavy business analyst.
Data science jobs available in the current market are as follows: 1. The Data Analyst : A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. “Data analysts' work varies depending on the type of data that they're working with (sales, social media, inventory, etc.) 2.The Data Engineer : Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data.
3. The Machine Learning Engineer : Machine learning engineer do one of the toughest job to create a ML or AI-based model that can work properly with best performance.
4.The Data Science Generalist : Data scientists analyze data, including data on customer behavior and preferences, to help businesses decide what direction they should be taking next. However, not every piece of data that a company gathers is clear or useful information. 5.Operation analyst : Operation analyst work as a consultant and even work internally in large organisation.His main focus is on internal processes of a business like internal reporting system, product manufacturing and distribution, and the general streamlining of business operations.
Data Science scope is very bright.there are lot of companies which hire Data Scienctists: 1. Startups: Ola cabs, Uber, Housing... 2. ECommerce: Snap deal, Flip-kart, Amazon... 3.Product based MNCs: Microsoft, VMware, Honeywell... 4.Service based analytics company: Mu-Sigma, Vizury, Fractal Analytics, Impetus, TCS...
There are many job openings for data scientists in private enterprises, public domain as well as in bigger corporations.
1.Data Scientist One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance data scientists are no longer just success stories for global giants such as Google, LinkedIn, and Facebook.
Roles & Responsibilities:
“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business
2.Data Engineer/Data Architect Data engineers are the designers, builders and managers of the information or big data infrastructure.Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Roles & Responsibilities:
Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path.
3.Machine Learning Engineer Machine learning has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
Roles & Responsibilities:
Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.
4.Big Data Engineer What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Roles & Responsibilities:
“Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive, MongoDB or Cassandra.
Systems analysts use and design systems to solve problems in information technology. The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests.
2. Healthcare Data Analyst
Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis. The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs.
3. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants. Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
4. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
6. Quantitative Analyst
A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc.
7. Data Analytics Consultant
Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time. They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.
8. Digital Marketing Manager
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset. Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
9. Project Manager
Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes. Project managers need at least a working understanding of data analytics, and often more. These positions are found internally at large corporations, and frequently in management consulting.
10. Transportation Logistics Specialist
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon or an supply chain organization.
1.Data Scientist One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance data scientists are no longer just success stories for global giants such as Google, LinkedIn, and Facebook.
Roles & Responsibilities:
“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business
2.Data Engineer/Data Architect Data engineers are the designers, builders and managers of the information or big data infrastructure.Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Roles & Responsibilities:
Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path.
3.Machine Learning Engineer Machine learning has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
Roles & Responsibilities:
Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.
4.Big Data Engineer What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Roles & Responsibilities:
“Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive, MongoDB or Cassandra.
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
List of Job Roles in Data Science in current market 1. MIS Reporting Executive Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization. 2. Business Analyst Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity. 3. Data Analyst Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it. 4. Statistician Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.
6. Data Engineer/Data Architect “Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data
8. Big Data Engineer What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
According to the sources a market study gives a view of the changes in market and highlights how employees need to develop skills in data science. The reports clearly demonstrates while skills in Python and Java are highly sought after, a professional programmer or the data analyst should hav the ability to learn new coding language. The trends in data science jobs are as follows : Job Openings in Data science sector: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
2) Forecasting annual growth in data science professionals : Compared to the numbers in 2017, in 2019 year there has been an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared t0 previous years .
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India.
Data scientist is the most demanded job in 21st century.with the surge and is correlate fields, the job of a data scientist has become the most sought after. Many IT professionals and academicians who have worked in quantitative fields want to become data scientists. The demand for data scientist is only increasing and will continue to increase in future Data science over the next few decades: it is predicted to grow over the next. It is a staggering fact that over 90% of the data in the world was generated on just 2 years It is rather an undefined and cured term. It is general term that has several definition with the passage of time, the data science roles will become more concretized. There will be a concise defination that will imparted to data science that will enable the scientist to handle corresponding operation.deeper career paths will be developed in data science.there is a diversification of role of data scientist. The rise in demand for data scientists will prompt educational institutes to include it in there curriculum. The data literacy will increase in future
General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data. Quantitative analyst - quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
Data architect: A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture. Machine learning scientists: They research new data approach and algorithms Companies: Apple, Tinder
Data Analyst:- A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data Scientist:- A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
Data Engineer:- A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
Data science includes statistics, data analysis, machine learning, and computer science. Companies provide jobs depending on the experience levels of the candidates. There are different types of data science jobs available in the market. Such as,
*Data analyst: Data analyst is the one who performs tasks like pulling data out of SQL databases, operating excel and tableau, producing basic data visualisations and reporting. They take the lead on the company's Google Analytics account.
*Data Engineer: Data Engineer is the one who performs tasks like controlling the data traffic of the company when there is huge dat traffic. They set up the data infrastructure in order to keep the company moving forward.
*Machine learning engineer: There are no.of companies for whom their data is their product. So in that case, the data analysis or machine learning is intense. This is the ideal situation for someone who has formal maths,stats and physics background. They often focus on producing data-driven products.
*Data science generalist: Data science generalist joins the established team of data scientists. They perform analysis, touch production code, visualise data,etc.
1. Data Analyst: A data analyst's primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon. Data analyst is someone who collects, processes and performs statistical analysis of data also a data analyst can translate numbers and data into plain English in order to help organization and company to make better business decisions.
2. Data scientist: A data scientist is a person who is employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making. A data scientist can have more freedom to pursue their own ideas and experiment to find intresting patterns and trends in the data.
3. Data engineer: A data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large scale processing systems. They need to recommend and sometimes implement ways to improve data reliability, efficiency and quality. They are also responsible for building and maintaining infrastructure needed to store and quickly access past data.
4. Machine learning engineer: It means a data scientist who has specialized in machine learning. A machine learning engineer is more of a software engineering role that involves taking a data scientists analysis and turning it into deployable software. They are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
5. Quantitative analyst: They are also called as quants. They use advanced statistical analyses to answer questions and make predictions related to finance and risk. A person who analyses a situation or event, especially a financial market, by means of complex mathematical and statistical modelling.
6. Business intelligence analyst: It is a professional role where the individual is responsible for analysing data that is used by a business or organization. They generally support decison making. A BI analyst must be focused on analysing market and business trends.
7. Statistician: They are generally data scientists before it existed.
8. Business analyst: They help companies answer questions and solve problems. They require analyst to capture, analyze and make recommendations based on company's data.
9. System analyst: They are often tasked with identifying organisational problems and then planning and overseeing the changes or new systems required to solve those problems.
1) Data Scientist is most likely one of the most sizzling job titles that you can have these days 2) The Data Analyst Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity 3)The Database Administrator As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save 4)Business analyst A business analyst therefore often performs the role of the middle person between the business folks and the techies. 5)Qualitative expert they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker.
Data analyst- This is typically considered an entry level position in the data science field, although not all data analysts are junior and salaries can range widely. A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.
Business Analyst Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest terms, a business analyst helps companies answer questions and solve problems. This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them. But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make you a more compelling candidate for almost any business analyst role.
Systems Analyst Systems analysts are often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems. This typically requires programming skill (although systems analysts are not always directly involved in developing the systems they recommend) and data analysis and statistical skills are also frequently necessary for identifying problematic trends and quantifying what’s working well and what isn’t within a company’s tech systems.
Marketing Analyst Marketing analysts look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. In the digital age, these analysts have access to increasingly large amounts of data, particularly at companies that sell digital products, and while there are a variety of software solutions like Google Analytics that can allow for decent analysis without programming skills, an applicant with data science and statistics chops is likely to have a leg up on many other applicants if they also have sufficient domain knowledge in the area of marketing. Plus, a marketing analyst whose analyses make a significant impact can set their long-term sights on a Chief Marketing officer.
1. Data Analyst: A data analyst's primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon. Data analyst is someone who collects, processes and performs statistical analysis of data also a data analyst can translate numbers and data into plain English in order to help organization and company to make better business decisions.
2. Data scientist: A data scientist is a person who is employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making. A data scientist can have more freedom to pursue their own ideas and experiment to find intresting patterns and trends in the data.
3. Data engineer: A data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large scale processing systems. They need to recommend and sometimes implement ways to improve data reliability, efficiency and quality. They are also responsible for building and maintaining infrastructure needed to store and quickly access past data.
4. Machine learning engineer: It means a data scientist who has specialized in machine learning. A machine learning engineer is more of a software engineering role that involves taking a data scientists analysis and turning it into deployable software. They are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
5. Quantitative analyst: They are also called as quants. They use advanced statistical analyses to answer questions and make predictions related to finance and risk. A person who analyses a situation or event, especially a financial market, by means of complex mathematical and statistical modelling.
6. Business intelligence analyst: It is a professional role where the individual is responsible for analysing data that is used by a business or organization. They generally support decison making. A BI analyst must be focused on analysing market and business trends.
7. Statistician: They are generally data scientists before it existed.
8. Business analyst: They help companies answer questions and solve problems. They require analyst to capture, analyze and make recommendations based on company's data.
9. System analyst: They are often tasked with identifying organisational problems and then planning and overseeing the changes or new systems required to solve those problems.
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.
Data Science is considered as an entirely new career field. Data Science is related to Data Mining and Big Data. Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Data Science Jobs Available in the Current Market are as follows:- 1) Data Analyst 2) Data Scientist 3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data. A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills. Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
Role and resposibility of An Data scientist
-Work with stakeholders to determine how to use business data for valuable business solutions -Search for ways to get new data sources and assess their accuracy -Browse and analyze enterprise databases to simplify and improve product development, marketing techniques, and business processes -Create custom data models and algorithms
skills required
-A natural inclination toward solving complex problems -Knowledge/experience on/with statistical programming languages, including R, Python, SLQ, etc., to process data and gain insights from it -Experience using and developing data architectures -Knowledge of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, etc., and their pros and cons -Knowledge and application experience in advanced statistical techniques and concepts, including, regression, distribution properties, statistical testing, etc. -Good communication skills to promote cross-team collaboration -Impulse to learn and master new technologies -Multilingual coding knowledge/experience: Java, JavaScript, C, C++, etc. -Experience/knowledge in statistics and data mining techniques, including, random forest, GLM/regression, social network analysis, text mining, etc.
Data analyst- This is typically considered an entry level position in the data science field, although not all data analysts are junior and salaries can range widely. A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.
Business Analyst Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest terms, a business analyst helps companies answer questions and solve problems. This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them. But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make you a more compelling candidate for almost any business analyst role.
Systems Analyst Systems analysts are often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems. This typically requires programming skill (although systems analysts are not always directly involved in developing the systems they recommend) and data analysis and statistical skills are also frequently necessary for identifying problematic trends and quantifying what’s working well and what isn’t within a company’s tech systems.
Marketing Analyst Marketing analysts look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. In the digital age, these analysts have access to increasingly large amounts of data, particularly at companies that sell digital products, and while there are a variety of software solutions like Google Analytics that can allow for decent analysis without programming skills, an applicant with data science and statistics chops is likely to have a leg up on many other applicants if they also have sufficient domain knowledge in the area of marketing. Plus, a marketing analyst whose analyses make a significant impact can set their long-term sights on a Chief Marketing officer.
1. IT Systems analysts: System analyst use and design systems to solve problems in information technology.Some system analyst use existing third party tools to test software within a company.
2. Health care data analyst: Health care data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientist find answers to the questions and problems they encounter on a daily basis.
3.Operational analyst: operations analysts are usually found internally at large companies but may work as consultants. they focus on the internal processes of a business. This can include internal reporting systems,product manufacturing and distribution and the general streaming of business operations.
4. Data Scientist: much like analysts in other roles, data scientist collect and analyze data and communicate actionable insights. Data scientists understand data from a more informed perspective to help make predictions.
5. Data Engineer: Data engineers often focuses on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes. They also need to upgrade a database infrastructure for faster queries.
6.Quantitative analysts: This is another highly sought after professional, especially in financial firms. Quantitative analysts use data analytical to seek out potential financial investment opportunities or risk management problems.
7. Digital marketing manager: Digital marketing requires a strong knowledge of data analytics. Marketers often use tool like google analytics, custom reporting tools and other third party sites to analyse traffic from websites and social media advertisements.
8. Project manager: These managers use analytics tool to keep track of a team's progress, track their efficiency and increase productivity by changing processes. Project manager need at least a working understanding of data analytics and often more.
9. Transportation logistics specialist: A transportation logistics specialist optimises transportation of physical goods and could be found in large hipping companies. The transportation logistics specialist need to reliably identify the most efficient paths for products and services to be delivered.
General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data. Quantitative analyst - quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
Data architect: A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture. Machine learning scientists: They research new data approach and algorithms Companies: Apple, Tinder
Data Analyst:- A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data Scientist:- A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
Data Engineer:- A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
1.Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average for B.A.+ jobs of 45 days.1 Requirements for data science and analytics jobs are often multidisciplinary and they all require an ability to link analytics to creating value for the organization. The analytics and technology skills vary widely, but candidates must also demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork. This holistic skill set is rare, so you should expect to compete fiercely for T-shaped individuals, as they are now often called, meaning those with a principle competency, plus well-honed broad skills to help them cross functions or domains.
2.In essence, there are two different markets for data science and analytics jobs. Across the ecosystem, we see two broad families: analytics-enabled jobs and data science jobs.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data.
Various data science jobs available in the current markets.
1) Data Analyst-B2B Marketing The B2B Marketing data analyst will spend most of their time analyzing data that includes past trends, current conditions, projected etc. This job requires a strong candidate with strong mindset with problem solving skill, technical acumen, and ability to solve the business problems. Analysis of data to provide assessments of trends, to identify and explain variances from goals, between periods and across teams to identify possible performance issues or challenges.
2) Data Insights and Analyst This job requires prior experience in business analytics and knowledge of related analysis or visualization tools. Expecting a minimum of 2-4 years of relevant experience.
3) Machine learning Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Responsibility is to build and manage actionable sales Analytics solution and KPIs to continually improve process and develop analyticanalytics products and manage the featurefeature roadmap for analytical components.
4) Sales Planning Analysts The position is responsible to support the global sales organization by assisting development of Strategic plans and Business Process using data science and Business intelligence technologies.
5) Digital Data Analytics Digital analytics is the process of analysing digital data from various sources like website, mobile application etc. It is tool usedby organization for collecting, measuring, and analyzing the qualitative and quantitative data.
6) Product Manager The product manager is responsible for defining the why, when and what itmf the product that engineering team builds. The Product Manager role is further complicated by how much decision-making power the product management function gets in the organization.
Mitali B Nevagi 2GI19MBA53 Data science process in collecting, storing, segregating and analyzing data which serves as a valuable resources to the organisation to carry out data and take important decisions they use highly skilled computer professionals. Big data analysis is the best career today to choose. They help the organisation to deal with costs, increases efficiency and also recognize new market opportunities. Data scientists are highly educated and boast of intelligence and a certain skill set relevant to the field. Some of the data science jobs are: Business intelligence developer Data architect Infrastructure architect Enterprise architect Data analyst Date engineer Machine learning scientist Statistician Data science experts are requ and valued in almost every field today. Many businesses and government depend on big data to provide effective services.
Data science is the field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining. Data scientists specific task include following: •Identifying the data-analytics problems that offer the greatest opportunities to the organization. •Determining the correct data sets and variables. •Collecting large sets of structured and unstructured data from disparate sources. •Cleaning and validating the data to ensure accuracy, completeness, and uniformity. •Devising and applying models and algorithms to mine the stores of big data. •Analyzing the data to identify patterns and trends. •Interpreting the data to discover solutions and opportunities. •Communicating findings to stakeholders using visualization and other means.
Data science helps in building better future for both an individual and companies. Some of the data science jobs are: 1. Business Intelligence Developer
BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems.
2. Data Architect Ensure data solutions are built for performance and design analytics applications for multiple platforms.
3. Data Architect Ensure data solutions are built for performance and design analytics applications for multiple platforms.
4. Data Analyst Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.
5. Data Engineer Perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists.
6. Machine Learning Scientist Research new data approaches and algorithms.Create data funnels and deliver software solutions.
7. IT Systems Analyst Systems analysts use and design systems to solve problems in information technology. The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Data science benefits both companies and consumers alike.
1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
2) Forecasting annual growth in data science professionals in 2018: Compared to the numbers in 2017, last year had an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared to 2017.
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities
7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers
Highlights While, it is difficult to ascertain the exact number of open analytics job openings; according to our estimates, close to 97,000 positions related to analytics & data science are currently available to be filled in India. This is almost 45% jump in the open job requirements, compared to same time a year back. Compared to worldwide estimates, India contributes 6% of open job openings currently. Growth in the number of data science jobs globally was much higher than India Last year India contributed 10% of worldwide open job requirements which has decreased to 6% this year, even though there have been an overall growth in numbers 10 leading organisations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte Almost 97% of analytics jobs advertised in India are of full-time basis. Just 3% form the part-time, internship or contractual jobs Top designations advertised are: Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant, Statistical Analyst, Hadoop Developer
Data science experts are needed in virtually every job sector—not just in technology. Here are some of the leading data science careers you can break into with an advanced degree.
1. Business Intelligence (BI) Developer: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems Notable Companies: DollarShave Club, Discover, and Liberty Mutual
2. Data Architect: The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms. Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Applications Architect: The job is to track the behavior of applications used within a business and how they interact with each other and with users. Notable Companies: UPS, Humana, Dow Jones, Oracle
4. Infrastructure Architect: They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy. Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
5. Data Scientist: They Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical. Notable Companies: Facebook, Capital One, Airbnb, Twitter
6. Data Analyst: The data analyst transforms and manipulates large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. Notable Companies: Walmart, Gap, Bank of America, Kohler.
Data science is most demanded skill in the market today both, in India as well al globally.the scope of Data science is increasing day by day.
Data analysis jobs hepls the following: -data engineer: he is the person who manages company's data infrastructure. This includes lot of statistics, calculations.
-data architect: he manages the data related to the architecture, the design maps, the layouts etc..
According to data science institution experts, the data science market is to grow in size to at least one-third of the IT. It says the the demand and the supply of the data analyst are not according to the expected number.
Around 70% of the data science job openings were for candidates with less than five years of experience, of which 21% were for pure freshers. Another 31% of open positions were offered to professionals with more than five years of industry work experience. The high demand for a data science professional has got most software engineers reskilling them to cater to the needs of the data science industry.
Data analysis plays an important role in the market as it helps the company, thr economy to collect the various data.
Data analysis deals with he major calculations of various data in terms of statistics, algorithms etc..
Data analyst: converts the numerical data into an understanding data is in a specific language, where it helps the company to know exact status of their product. Like how many people has the product attracted, or been sold, or been purchased. This will help the company to make further decision for the company but analysing the above facts as driven by the data analyst.
Data science job opportunities in future market: 1 Data warehouse architect: This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst: The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician: statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst : marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
Data science job opportunities in future market: 1 Data warehouse architect: This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst: The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician: statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst : marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
Data science experts are needed in virtually every job sector—not just in technology. Here are some of the leading data science careers you can break into with an advanced degree.
1. Business Intelligence (BI) Developer: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems Notable Companies: DollarShave Club, Discover, and Liberty Mutual
2. Data Architect: The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms. Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Applications Architect: The job is to track the behavior of applications used within a business and how they interact with each other and with users. Notable Companies: UPS, Humana, Dow Jones, Oracle
4. Infrastructure Architect: They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy. Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
5. Data Scientist: They Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical. Notable Companies: Facebook, Capital One, Airbnb, Twitter
6. Data Analyst: The data analyst transforms and manipulates large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. Notable Companies: Walmart, Gap, Bank of America, Kohler.
1) Data Scientist as Statistician This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields.Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions. 2) Data Scientist as Mathematician Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms. 3) Data Scientists Vs Data Engineers These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. Additionally, he is also responsible for its smooth functioning. They need to work closely with data scientists, IT managers and other business leaders to translate raw data into actionable insights which would result in competitive edge for the organization. 4) Data Scientist as Machine Learning Scientists Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.). 5) Data Scientist as Business Analytic Practitioners Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts. They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc.
Data science job opportunities in future market: 1 Data warehouse architect: This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation. 2. Data Architect: The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms. Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3 statistician: statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4. Infrastructure Architect: They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy. Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
1.Business Intelligence (BI) Developer: A business intelligence developer is responsible for designing and developing BI solutions, overseeing the implementation of the BI system and also the ongoing maintenance of those solutions. 2. Data Architect : A data architect is a practitioner of data architecture ,a data management discipline concerned with designing, creation, deploying and managing an organizations data architecture. 3. Applications Architect : application architecture or application architecture is one of several architecture domains that form the pillars of enterprise architecture. 4. Infrastructure Architect: Infrastructure Architects design and implement information systems to support the enterprise infrastructure of an organization. They ensure that all systems are working at optimal levels and support the development of new technologies and system requirements. 5. Data Analyst: A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. 6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists.
Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average. The following are the list of job data science are: 1. Business analyst- business analyst are experts in the domain they work in.they have the narrow gap between the business and information technology.business analyst provides solutions that are often technology. 2.Data analyst- data analyst are more of generalist.they play a measure role from acquiring Massive amounts of data to processing and summarising it. 3. Data engineer or data architect- data enginner are the designers,builders and managers of the information or big data infrastructure.data engineers ensure that an organisations big ecosystem. 4. Machine learning engineer- Machine learning has become quite a booming field with the mind boggling amount of data.engineers should focus on python,java,c++ etc. 5. MIS reporting executive- business manager rely on management information system reports to automatically track progress,make decisions and indentify problems. 6. Data scientist- this is one of the most in demand professionals today data scientist rule roost of number.data scientist are no longer they were scripting success stories are Google, LinkedIn and Facebook. 7. Big data engineer - big data solutions architect designs,big data engineer builds.big data is a big domain every kind of role has its own specific responsibilities. Shreya Sainuche 2GZ19MBA34
List of Job Roles in Data Science / Big Data 1. MIS Reporting Executive Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
2. Business Analyst Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
3. Data Analyst Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it
4. Statistician Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.
Almost every company has some sort of a data role on its careers page. Job Descriptions for data scientists and data analysts show a significant overlap. 6. Data Engineer/Data Architect “Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
8. Big Data Engineer What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Descriptive analytics Descriptive analytics answers the question of what happened. Let us bring an example from ScienceSoft’s practice: having analyzed monthly revenue and income per product group, and the total quantity of metal parts produced per month, a manufacturer was able to answer a series of ‘what happened’ questions and decide on focus product categories.
Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, our data consultants don’t recommend highly data-driven companies to settle for descriptive analytics only, they’d rather combine it with other types of data analytics.
Diagnostic analytics At this stage, historical data can be measured against other data to answer the question of why something happened. For example, you can check ScienceSoft’s BI demo to see how a retailer can drill the sales and gross profit down to categories to find out why they missed their net profit target. Another flashback to our data analytics projects: in the healthcare industry, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) allowed identifying the influence of medications.
Diagnostic analytics gives in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal, otherwise, data collection may turn out to be individual for every issue and time-consuming. Predictive analytics Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Check ScienceSoft’s case study to get details on how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
Predictive analytics belongs to advanced analytics types and brings many advantages like sophisticated analysis based on machine or deep learning and proactive approach that predictions enable. However, our data consultants state it clearly: forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires careful treatment and continuous optimization.
Prescriptive analytics The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from our project portfolio: a multinational company was able to identify opportunities for repeat purchases based on customer analytics and sales history.
Prescriptive analytics uses advanced tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage. Besides, this state-of-the-art type of data analytics requires not only historical internal data but also external information due to the nature of algorithms it’s based on. That is why, before deciding to adopt prescriptive analytics, ScienceSoft strongly recommends weighing the required efforts against an expected added value.
Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further.
Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreak hit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic.
Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.
In a 2015 speech, Economist and Freakonomics author Steven Levitt said that CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization […] is absolutely going to be at the center of what economics is and what other social sciences are going forward.”
Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.
Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further.
Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreak hit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic.
Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.
In a 2015 speech, Economist and Freakonomics author Steven Levitt said that CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization […] is absolutely going to be at the center of what economics is and what other social sciences are going forward.”
Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.
Information or data is the new oil, and there is an ever increasing demand for millions of professionals across the world to drill into that data, clean it, refine it, infer insights and sell it to the world at a premium. Hence, there is a huge demand for analytics professionals who understand this domain. By combining the power of science, art, and technology. Data science jobs available in the current market are as follows: 1.Data analyst :Analytics is broadly defined as the systematic computational analysis of data or statistics and analysts lie at the confluence of three streams which are data science skills, industry/domain understanding, and technology advancements. 2.Data scientist:The job of the data scientist is the same as that a statistician’s role, incorporating the use of advanced analytics and leading to accurate predictions which are essential for business. 3. Data Engineer:Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages. 4. Machine learning engineer:Machine Learning is a technical tool of data science that creates logic from data by transforming data into knowledge. It provides efficient analysis models for capturing knowledge by improving prediction for data driven decisions. It plays an eminent role in the field of computer science that paves its way in analyzing robust emails, spam filters, convenient text, voice recognition, web search engines, game developments and self – driving cars.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
4 Types of Data Science Jobs 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google .
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
Data Science Job Market Is Booming Analytics professionals command higher compensation and outearn their peers considerably. As per data from our Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals. Furthermore, this year, the entry level professionals ((0-3 years experience) saw a slight increase in salaries, with compensation going up from ₹5.2L median last year to ₹5.3L per annum. At the entry level, almost 76% of analytics professionals earn under 6 lakh per annum.
Enterprises Double Down on Analytics Initiatives As organisations continue to widen their analytics efforts, there is a rising demand for analytics professionals who work at the intersection of data and business, can prioritise business problems, and can help organisations achieve real impact from their data-driven initiatives. Here’s What the Industry Demands Strong educational background: In order to work with agile teams, organisations look out for candidates with a Bachelor’s degree or MS in quantitative fields like engineering, Math, Statistics and more popularly CS. In most cases, an MS is a huge advantage.
Tool-chain: Knowledge of programming like Python and R is a must to break into the data science field. In addition to this, one should also have proficiency in statistical packages such as SAS or SPSS that are also used in organisations. Experience working with large datasets: As data grows by leaps and bounds, enterprises are dealing with both structured and unstructured data. Our study confirms that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Domain knowledge: Data analytics professionals have to work at the intersection of business and technology and drive result-driven projects. One of the key aspects is to understand key operational metrics and the impact on business. In addition, knowledge of common use cases such as churn prediction (telecom), customer acquisition (e-marketplaces), inventory management (e-marketplaces), predictive maintenance (manufacturing) can be a plus.
1) Data Scientist is most likely one of the most sizzling job titles that you can have these days 2) The Data Analyst Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity 3)The Database Administrator As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save 4)Business analyst A business analyst therefore often performs the role of the middle person between the business folks and the techies. 5)Qualitative expert they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker.
. MIS Reporting Executive Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization 2. Business Analyst Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity 3. Data Analyst Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
In our annual study with Great Learning, we examine the landscape of job requiring data science and analytics competencies and skills. Our market snapshot gives a quick view of the evolving talent market and highlights how employees need to develop a blend of skills to strive ahead in data science roles. The report clearly demonstrates while skills in Python and Java are highly sought-after, a professional programmer or a data analyst should have the ability to learn new coding languages. It also highlights the need for work-based learning opportunities which will help employees gain the necessary skills. Many companies have mentor-style programmes to help employees understand and upgrade the skill-set needed at the workplace.
The report seeks to provide forward-looking insights for recruiters and hiring companies to build a detailed understanding of their talent needs, counter the skills gap in their workforce strategy and build a talent pipeline and also highlight the talent hotspots in India. The research presented in this report has been collected through a mix of analysis from publicly available data and other information sources.
Open Analytics Jobs in India Top Trends In Data Science Jobs 1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual Data science job opportunities in future market: 1 Data warehouse architect: This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst: The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician: statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst : marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
Data science jobs and responsibilities are diverse in the current market. Few of them are as follows:
1. The Data Scientist: A data scientist is as rare as a unicorn and gets to work everyday with the mindset of a curious data wizard. He/she masters a whole range of skills and talents going from being able to handle the raw data, analyzing that data with the help of statistical techniques, to sharing his/her insights with his peers in a compelling way. No wonder these profiles are highly wanted by companies like Google and Microsoft.
2. The Data Analyst: The data analyst is the detective of the data science team. Languages like R, Python, SQL and C are mandatory to him/her. Data analysts are wanted by companies like HP and IBM (where they can be teamed up with Watson).
3. The Data Architect: With the rise of big data, the importance of the data architect’s job is rapidly increasing. The person in this job creates the blueprints for data management systems to integrate, centralize, protect and maintain the data sources. The data architect masters technologies like Hive, Pig and Spark, and needs to be on top of every new innovation in the industry.
4. The Statistician: The statistician represents what the data science field stands for: getting useful insights from data. With his/her strong background in statistical theories and methodologies, and a logical and stats oriented mindset, he/she harvests the data and turns it into information and knowledge. Statisticians can handle all sorts of data.
5. The Business Analyst: The business analyst is often a bit different from the rest of the team. While often less technically oriented, the business analyst makes up for it with his/her deep knowledge of the different business processes. Companies looking for business analysts are diverse and active in very different industries. Some examples are Uber, Dell and Oracle.
Data science is a detailed study of the flow of information from the colossal amounts of data present in an organization's repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills. The following are the data science jobs currently in the market:- 1. DATA ANALYTICS:- Data analytics is the science of analyzing raw data in order to make conclusions about that information. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. 2 DATA SCIENTISTS:- A person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making. 3 STATISTICIAN:- A statistician is a person who works with theoretical statistics. The profession exists in both the private and public sectors. 4 DATA ARCHITECT:- Data architect creates blueprints for data management systems. After assessing a company's potential data sources, architects design a plan to integrate, centralize, protect and maintain them. 5. DATA ENGINEER:- They are just like data scientists, but tend to be less visible because they tend to be further from the end product of the analysis. 6. MARKETING ANALYST:- Marketing analyst is the practice of managing and studying metrics data in order to determine the ROI of marketing efforts as well as the act of identifying opportunities for improvement.
This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
A series of software tools and algorithms will be used to ingest, filter and highlight data that can be further analyzed.
You’d be forgiven for thinking automation would make data scientists obsolete. A lot of negative talk surrounds automation, particularly about uprooting jobs and careers. That’s not the case in data science, however.
Experts and experienced scientists will still be needed to highlight, identify and implement actionable insights. You can expect to see a lot of automation platforms crop up in the industry over the coming year.
2. Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
Data empowerment is about the alignment or collaboration of everyone involved in a system. It means that everyone has access to the same tools and resources and the same data stores.
More importantly, it means putting data in the hands of the right people — those who can make use of it.
3. Ethics and Influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
The point here is not that the ethics or influence of the industry and related systems will change over the coming year, but that discussions will continue. New applications for data science are discovered on an almost daily basis. Data engineers, scientists, analysts and even administrators will need to join the discussion to let others know now this technology can help others.
After all, you are the ones responsible for creating, developing and maintaining these technologies and systems. What is it you have to share with the world? What can you explain or provide insights about?
You could introduce the positive side of data analytics and collection, for instance, by explaining how it’s helping to fight modern battles and keeping America safer. Or how it’s providing modern conveniences to many — conveniences like personalized shopping campaigns, better home security and more.
2018 will be the year that data science professionals become a part of the greater discourse.
4. Data Lakes and Mass Cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
Cleaning a swamp and converting it into a data lake calls for categorizing, attaching relevant metadata and sorting everything into the appropriate storage segments.
Expect a boom in data restructuring as more organizations and parties realize how beneficial data lakes are.
5. Blockchain App Development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
This attention will call for more demand in the development world as teams look to work with blockchain and implement it into their products, services and systems. In the financial and healthcare industries, for example, a lot of work is being done to introduce blockchain-powered platforms.
The Th Data Science Jobs Available in the Current Market are as follows:- 1) Data Analyst 2) Data Scientist 3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data. A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills. Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format
Data science is a multidisciplinary blend of data inference, algorithmm development and technology in order to solve analytically complex problems. Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. Data science is ultimately about using this data in creative ways to generate business value like Data Warehouse,Discovery of Data Insight etc 1-Data science – discovery of data insight This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:
Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce. Target identifies what are major customer segments within it's base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences. Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally. 2-Data science – development of data product A "data product" is a technical asset that: (*) utilizes data as input, and (*) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data. Here are some examples of data products:
Amazon's recommendation engines suggest items for you to buy, determined by their algorithms. Netflix recommends movies to you. Spotify recommends music to you. Gmail's spam filter is data product – an algorithm behind the scenes processes incoming mail and determines if a message is junk or not. Computer vision used for self-driving cars is also data product – machine learning algorithms are able to recognize traffic lights, other cars on the road, pedestrians, etc. This is different from the "data insights" section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision. In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications. Respective examples of applications that incorporate data product behind the scenes: Amazon's homepage, Gmail's inbox, and autonomous driving software. Types of Data Science Jobs 1. The Data Analyst: There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. 2.The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills. 3.The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. Dixita.s.savant
1. Data analyst : This is typically considered an “entry-level” position in the data science field, although not all data analysts are junior and salaries can range widely.A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon. 2.Data scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.As a data scientist, you might be asked to assess how a change in marketing strategy could affect your company’s bottom line. This would entail a lot of data analysis work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make reliable future predictions based on past data. 3. Data engineer A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
MIS Reporting Executive Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems.
Business Analyst Business analysts are experts in the domain they work in. They try to narrow the gap between business and IT.
Data Analyst Data analysts they play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
Statistician Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions.
Data Scientist Data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance.
Data Engineer Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Machine Learning Engineer Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into.
Big Data Engineer Big data is a big domain, every kind of role has its own specific responsibilities.
1.Data Analyst A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data Scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data.
3.Data Engineer A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill.
Prachprachishivangekar 2GZ19MBA14 1) Data Scientist is most likely one of the most sizzling job titles that you can have these days 2) The Data Analyst Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity 3)The Database Administrator As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save 4)Business analyst A business analyst therefore often performs the role of the middle person between the business folks and the techies. 5)Qualitative expert they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker
Data Science Job Market Is Booming Analytics professionals command higher compensation and outearn their peers considerably. As per data from our Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals. Furthermore, this year, the entry level professionals ((0-3 years experience) saw a slight increase in salaries, with compensation going up from ₹5.2L median last year to ₹5.3L per annum. At the entry level, almost 76% of analytics professionals earn under 6 lakh per annum.
1.business intelligent analyst: It includes the technology, applications and practices for collection,integration, analysis and presentation of business information. 2.data mining engineer: These jobs develop set processes for data mining,and data modeling, and data production. 3. Data architect: A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture. 4.data scientists They are the big data wranglers,gathering and analyzing large sets of structures and unstructured data. A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. 6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists. A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. 6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists. A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. 6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists.
This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
A series of software tools and algorithms will be used to ingest, filter and highlight data that can be further analyzed.
You’d be forgiven for thinking automation would make data scientists obsolete. A lot of negative talk surrounds automation, particularly about uprooting jobs and careers. That’s not the case in data science, however.
Experts and experienced scientists will still be needed to highlight, identify and implement actionable insights. You can expect to see a lot of automation platforms crop up in the industry over the coming year.
2. Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
Data empowerment is about the alignment or collaboration of everyone involved in a system. It means that everyone has access to the same tools and resources and the same data stores.
More importantly, it means putting data in the hands of the right people — those who can make use of it.
3. Ethics and Influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
The point here is not that the ethics or influence of the industry and related systems will change over the coming year, but that discussions will continue. New applications for data science are discovered on an almost daily basis. Data engineers, scientists, analysts and even administrators will need to join the discussion to let others know now this technology can help others.
After all, you are the ones responsible for creating, developing and maintaining these technologies and systems. What is it you have to share with the world? What can you explain or provide insights about?
You could introduce the positive side of data analytics and collection, for instance, by explaining how it’s helping to fight modern battles and keeping America safer. Or how it’s providing modern conveniences to many — conveniences like personalized shopping campaigns, better home security and more.
2018 will be the year that data science professionals become a part of the greater discourse.
4. Data Lakes and Mass Cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
Cleaning a swamp and converting it into a data lake calls for categorizing, attaching relevant metadata and sorting everything into the appropriate storage segments.
Expect a boom in data restructuring as more organizations and parties realize how beneficial data lakes are.
5. Blockchain App Development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
This attention will call for more demand in the development world as teams look to work with blockchain and implement it into their products, services and systems. In the financial and healthcare industries, for example, a lot of work is being done to introduce blockchain-powered platforms.
1.Data Analyst A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data Scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data.
3.Data Engineer A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
4 Types of Data Science Jobs 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. 2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. 3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path. 4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
1. MIS Reporting Executive Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
2. Business Analyst Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
3. Data Analyst Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
4. Statistician Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook. Almost every company has some sort of a data role on its careers page. Job Descriptions for data scientists and data analysts show a significant overlap.
6. Data Engineer/Data Architect “Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
8. Big Data Engineer What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Data analyst - he is a person who looks at the industry data and uses it to ans the business questions and communicates them to other team members
Data scientist - they do similar things like the data analysits but also build machine learning models to make accurate predictions using the past data for the future
Data engineer - he manages a company's data infrastructure. They require more of a software development and programming skill. They are responsible for building data pipeline to get the latest aalessales, marketing data and in a usable format
The data science industry’s job market is hot today, which means that one way to better understand the meaning behind different data science roles is by having a detailed look at all job offerings, their description and their skill requirements.
1)The Data Engineer The data engineer often has a background in software engineering and loves to play around with databases and large –scale processing systems.
2)The Data Analyst The data analyst is the Sherlock Holmes of the data science team. Languages like R, Python, SQL and C are elementary to him/her. Just like the data scientist, the skills and talents that are needed for this role are diverse and range the entire spectrum of the data science process combined with a healthy “figure-it-out” attitude. Data analysts are wanted by companies like HP and IBM .
3)The Data Engineer The data engineer often has a background in software engineering and loves to play around with databases and large –scale processing systems. Thanks to these interests, he/she can easily master technologies and is therefore familiar with a diverse set of languages that span both statistical programming languages and languages oriented more towards web development.
4)The Statistician Ah the statistician! The historical leader of data and its insights. Although often forgotten or replaced by fancier sounding job titles, the statistician represents what the data science field stands for: getting useful insights from data. With his/her strong background in statistical theories and methodologies, and a logical and stats oriented mindset, he/she harvests the data and turns it into information and knowledge. Statisticians can handle all sorts of data.
5)The Database Administrator People often say that data is the new gold. This means you need someone who exploits that valuable mine. Enter the Database Administrator. Your DA makes sure that the database is available to all relevant users, is performing properly and is being kept save.
6)The Business Analyst The business analyst is often a bit different from the rest of the team. While often less technically oriented, the business analyst makes up for it with his/her deep knowledge of the different business processes.
7)Data and Analytics Manager The cheerleader of the team. A data analytics manager steers the direction of the data science team and makes sure the right priorities are set.
Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average for B.A.+ jobs of 45 days.1 Requirements for data science and analytics jobs are often multidisciplinary and they all require an ability to link analytics to creating value for the organization.
The analytics and technology skills vary widely, but candidates must also demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork. This holistic skill set is rare, so you should expect to compete fiercely for T-shaped individuals, as they are now often called, meaning those with a principle competency, plus well-honed broad skills to help them cross functions or domains.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core.
Strategies for building your own data science and analytics talent pipeline
Here are three strategies to consider now:
Signal for competencies, not just skills, to build your data science team. A competency framework for your company's data science and analytics jobs is a clear signal to policymakers and education providers about what you need.
Identify your high-potential employees for data analytics work and invest in them. Work-based learning opportunities, where teams can work on real business problems, help people gain much-needed experience. Apprentice-mentor style arrangements or specialized development paths can help you pay attention to existing employees with potential for a future in data science.
Connect with other catalysts in your immediate communities. Employers can flex some muscle and influence policymakers and education providers to do more. Act as a community and join a collaborative or other organization focused on creating a competitive workforce for the 21st century.
Data science jobs available in the current market are: 1)Automated Machine Learning (AutoML) AutomML is growing in prominence. It is an automation layer on top of machine learning (ML), boosting the development efficiency and overall productivity of data scientists. The data science tasks like data preparation, choosing data sources, and feature selection are repetitive and monotonous. AutoML looks into automating these tasks. AutoML can also perform quality modelling and fine-tuning of artificial intelligence (AI) and machine learning algorithms. Giants like Facebook and Google are already using AutoML for their internal data science processes.
2) Quantum Computing Quantum computers are known to perform complex calculations in supersonic speed, completing the computational tasks in a few seconds. The quantum bits (QuBits) can store a large volume of data and run complex computations in split seconds. Companies like Intel, Google, and IBM, are going to be the pioneers of this industry, but they are yet in their humble beginnings.
3) Edge Computing With Data Science Edge computing is the technology by which the more amount of computation happens on end devices. Edge computing has pushed IoT one step ahead, and with the data science industry backing the end device computations, edge computing is all set to hit the markets in a big way.
4)Blockchain Blockchain is a highly secured ledger-based system that requires data science. Notably, data science has shaken hands with blockchain technology to implement new security measures and processes.
VINAY KAMAT(2GZ19MBA49) 1)Data Scientist: He has to Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical. 2) Data Analyst: He has to transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. 3) Data Engineer : he has to perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists. 4) Machine Learning Engineer: he has to create data funnels and deliver software solutions. 5) Statistician: he has to interpret, analyze, and report statistical information, such as formulas and data for business purposes. 6) Machine Learning Scientist: he has to research new data approaches and algorithms. 7) Infrastructure Architect: Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy. 8) Enterprise Architect: an enterprise architect, Works closely with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes and IT assets. 9) Business Intelligence developer: Business Intelligence developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems
Systems analysts use and design systems to solve problems in information technology. The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests.
2. Healthcare Data Analyst
Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis. The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs.
3. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants. Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
4. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
6. Quantitative Analyst
A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc.
7. Data Analytics Consultant
Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time. They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.
8. Digital Marketing Manager
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset. Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
9. Project Manager
Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes. Project managers need at least a working understanding of data analytics, and often more. These positions are found internally at large corporations, and frequently in management consulting.
10. Transportation Logistics Specialist
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon or an supply chain organization.
Data science jobs available in the current market : 1) Data Scientist as Statistician : Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2) Data Scientist as Mathematician Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientist as Machine Learning Scientists Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).
4) Data Scientist as Business Analytic Practitioners Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts. They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc. 5) Data Scientist as Software Programming Analysts Unlike traditional coders, this class of professionals have a knack for number crunching through programming. Needless to mention, they are adept at logical thinking and as a result, they take to new programming languages as ducks takes to water. A number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop and the like support data analytics and visualizations. Software programming analysts have the programming skills to automate routine big data related tasks to reduce computing time. They are also required to handle database and associated ETL (Extract Transform Learn) tools that can extract data, transform it by applying business logic and to load it into visual summary representations such as charts, histograms and interactive dashboards.
6) Data Scientist as Quality Analyst Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. The job has evolved over the years with new analytic tools which are used by data scientists to prepare interactive visualizations that serve as key inputs in decision making across teams such as management, business, marketing, sales and customer service.
Hayawadan Ayachit 2GI19MBA33 Data science jobs available in the current market : 1) Data Scientist as Statistician : Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2) Data Scientist as Mathematician Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientist as Machine Learning Scientists Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).
4) Data Scientist as Business Analytic Practitioners Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts. They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc. 5) Data Scientist as Software Programming Analysts Unlike traditional coders, this class of professionals have a knack for number crunching through programming. Needless to mention, they are adept at logical thinking and as a result, they take to new programming languages as ducks takes to water. A number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop and the like support data analytics and visualizations. Software programming analysts have the programming skills to automate routine big data related tasks to reduce computing time. They are also required to handle database and associated ETL (Extract Transform Learn) tools that can extract data, transform it by applying business logic and to load it into visual summary representations such as charts, histograms and interactive dashboards.
6) Data Scientist as Quality Analyst Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. The job has evolved over the years with new analytic tools which are used by data scientists to prepare interactive visualizations that serve as key inputs in decision making across teams such as management, business, marketing, sales and customer service.
Ninad Patil (2GZ19MBA03) 1. Automation : This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system. 2. Data lakes and mass cleanups At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized. 3. Block chain app development Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries. 4. Ethics and influence The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers. 5. Data empowerment Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
SHREYA KAPASE 2GZ19MBA33 Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. Different types: 1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills. 3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path. 4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
UnknownMarch 28, 2020 at 8:45 PM 1. Data analyst : This is typically considered an “entry-level” position in the data science field, although not all data analysts are junior and salaries can range widely.A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon. 2.Data scientist Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.As a data scientist, you might be asked to assess how a change in marketing strategy could affect your company’s bottom line. This would entail a lot of data analysis work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make reliable future predictions based on past data. 3. Data engineer A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields 2) Data Scientist as Mathematician
Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientists Vs Data Engineers
These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. Additionally, he is also responsible for its smooth functioning. They need to work closely with data scientists, IT managers and other business leaders to translate raw data into actionable insights which would result in competitive edge for the organization. 4) Data Scientist as Machine Learning Scientists
Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting.
Data science job is used to analyse data for actionable insights and it describes jobs that are drastically different from one another in current market structure .
1. The Data Analyst There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
3. The Machine Learning Engineer There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
Big Data Analytics Companies are used to analyze large-scale datasets that are too big to process using traditional methods. This Big Data Analytics Applications tutorial introduces the concept of Big Data Analytics Applications, including the goals, challenges, and techniques used to analyze them.
103 comments:
1.Data analyst in B2B Marketing : In B2B marketing data analysts will spend most of their time in analysing the data that includes the past trends,curren conditions etc.
2.Data Insights and Analyst : Hear he/she will be managing a team.Have keen eye for the detail and the quality
3.Analytics Manager : Here the person will build and manage actionable sales analytics solutions.
4.Chief Data Scientist : he has the responsibility to define the new product ideas with predictive and goal-oriented capabilities to market.Chief Data Scientist has responsibility for leading the research, development, and delivery of big data-based solutions
5.Associate Data Scientist : This person should be able to manage conformity to established procedures and processes.He will be acting as close driving agent with stakeholders.
6.Senior Analytics Manager : They will be developing predictive models in customer analytics.Track all scorecards- performances and highlight to management in case of any breaches/actions required.
7.Sales Planning Analyst : This position is responsible to support the Global Sales Organization .which helps in assisting the development of strategic plans and business processes by using Data science and Business Intelligence technologies.which is also focused towards the implementation of effective sales strategies to meet company objectives.
8.Data Engineer : this person will define, deliver, and support the data solutions. And they are also responsible for the setup, configuration, development, and ongoing operations of the company.
9.Product Manager :They will lead end-to-end roadmap of companies academic and delivery products in sync with business objectives.And also will manage all aspects of the product lifecycle, including problem definition, customer needs, requirement or usecase definition
10. General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data.
1. Machine learning engineer -
there is a lot of overlap between machine learning engineer and data scientist at some companies this title just means data scientist who has specialised in machine learning.Aadhar companies machine learning engineer is more of a software engineering role that involves taking data scientists analysis and turning it into deployable software. Aldo 10 specifix query, virtually all machines learning engineer positionswill require at least Tata thanks programming skills and a pretty advanced knowledge about machine learning techniques.
2. Quantitative analyst -
quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
3. Data warehouse architect-
essentially this is a speciality of field winding data engineering for 4 years would like to be in charge of companies data storage systems. SQL skills are definitely going to be important follow like this, also need a solid command of other technical skills that will vary based on the employers tech stack.
4. Business intelligence analyst-
Business analyst is essentially a data analyst who is focused on the analysing market and business trends. This position sometimes requires familiarity with software based data analysis tools but many data science skills are also crucial for business intelligence analyst position and many of these positions will also require python or r programming skills.
5. Statistician-
statistician is what data scientist where called before turn data scientist existed. Required skills can vary quite a bit bi from job to job but all of them will require a solid understanding of probability and statistics.
6. Business analyst-
business analyst is operator generic job title that's applied to a wide variety of rolls but Hindi broadest terms ab business analyst helps companies answer questions and solve problems.
7. System analyst-
system analyst are often task with current flowing organisational problems and then planning and overseeing the charges for new systems required to solve those problems.
8. Marketing analyst-
marketing analyst look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. in the digital age,these analyst have access to increasing Lee large amounts of data, particularly at companies that sell digital products, and while there are variety of software solutions like Google earth that can allow for decent analysis without programming skills and applicant with data science and statistics jobs is likely to have a leg up on many other applicants if they also have sufficient domain knowledge Hindi area of marketing.
9. Operation analyst-
operation analysts are typically task with examining and streamlining a business internal operations. Specific duties and salaries can vary widely, and not all operations analysts positions will make use data skills coma but in many cases, being able to clean analyse, and visualise data will be important in determining what company systems are working smoothly and what areas might need improvement.
Data science is improving and changing to a vast extent over the years .its has a lot of scope in current and future in companies, now companies hiring alot of data scientists.
It has considered as one of the top professional careers India.
The following are the skills required to become data scientists
1. Programming language
2. Analytical knowledge
3. Communication skills
4. Technical skills
5. Statistical knowledge.
*The following are the jobs available for the data science
1.Business intelligent analyst
2.data mining engineer
3.data architect
4.data scientists
5.senior data scientists
6.database system manager
Explanation:
1.business intelligent analyst:
It includes the technology, applications and practices for collection,integration, analysis and presentation of business information.
2.data mining engineer:
These jobs develop set processes for data mining,and data modeling, and data production.
3. Data architect:
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture.
4.data scientists
They are the big data wranglers,gathering and analyzing large sets of structures and unstructured data.
1. Business intelligence:Here BI developers design and develop strategies to assist business users in quickly finding the information that is needed in making better business decisions.
Companies: DollarShave Club, Discover, and Liberty Mutual.
2. Data Architect:Here he/she has to ensure that data solutions are built for performance and design analytics applications for multiple platforms.
Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Machine learning scientists: They research new data approach and algorithms
Companies: Apple, Tinder
4. Infrastructure Architect: According to Techopedia an enterprise architect works closely with stakeholders, including management and subject matter experts (SME),to develop a view of an organization’s strategy, information, processes and IT assets.
Companies: Cisco, Boeing, Lockheed Martin, Microsoft
5. Data scientists:He/she has to find, clean, and organize data for companies.
Companies: Facebook, Twitter
6. Data engineers: they have to perform batch processing or real time processing on gathered and stored data.
Companies: Spotify, Verizon, General Motors, Shutterfly
1) Job Openings in Data science sector in 2019 - There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
Forecasting annual growth in data science professionals in 2018: Compared to the numbers in 2017, last year had an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared t0 2017.
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecome.
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability.
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities.
7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers.
Some of the Data Science Jobs available in the current market are Data Analyst, Data Scientist and Data Engineer.
Other then these posts there are different job titles in Data Science such as Machine Learning Engineer, Quantitative Analyst, Data Warehouse Architect, Business Intelligence Analyst, Statistician Etc.
The Big Three are:
1. Data Analyst:-
A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2. Data Scientist:-
A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
3. Data Engineer:-
A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
There are two different markets for data science and analytics jobs. Across the ecosystem, we see two broad families: analytics-enabled jobs and data science jobs.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data.
Each of these markets requires its own strategy: sourcing from small pools of experienced data scientists and analysts for one, and employee development for the other.
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
“A company like this is a great place for an aspiring data scientist to learn the ropes.”
Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
“Machine Learning Engineers often focus more on producing great data-driven products than they do answering operational questions for a company.”
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
“Some of the most important ‘data generalist’ skills are familiarity with tools designed for ‘big data,’ and experience with messy, ‘real-life’ datasets.”
Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning
1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities
7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers
Some of the highlights are as follows:-
✓While, it is difficult to ascertain the exact number of open analytics job openings; according to our estimates, close to 97,000 positions related to analytics & data science are currently available to be filled in India.
✓This is almost 45% jump in the open job requirements, compared to same time a year back.
✓Compared to worldwide estimates, India contributes 6% of open job openings currently. Growth in the number of data science jobs globally was much higher than India.
✓Last year India contributed 10% of worldwide open job requirements which has decreased to 6% this year, even though there have been an overall growth in numbers.
✓10 leading organisations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte.
✓Almost 97% of analytics jobs advertised in India are of full-time basis. Just 3% form the part-time, internship or contractual jobs.
4 Types of Data Science Jobs
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
“A company like this is a great place for an aspiring data scientist to learn the ropes.”
Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
As a result, you’ll have great opportunities to shine and grow via trial by fire, but there will be less guidance and you may face a greater risk of flopping or stagnating.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
“Machine Learning Engineers often focus more on producing great data-driven products than they do answering operational questions for a company.”
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
“Some of the most important ‘data generalist’ skills are familiarity with tools designed for ‘big data,’ and experience with messy, ‘real-life’ datasets.”
Generally, these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.
Data science job is used to analyse data for actionable insights and it describes jobs that are drastically different from one another in current market structure .
In more general terms a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. This process requires persistence, statistics, and software engineering skills & it also includes the skills that are also necessary for understanding biases in the data, and for debugging, logging output from code.
It consist of a specific tasks that includes the following:
1. Identifying the data analytics problems that offer the greatest opportunities to the organization.
2. Determining the correct data sets and variables.
3. Collecting large sets of structured and unstructured data from Disparate sources.
4. Cleaning and validating the data to ensure accuracy, completeness, and uniformity.
5. Devising and applying models and algorithms to mine the stores of big data.
6. Analysing the data to identify patterns and trends.
7. Communicating findings to stakeholders using visualization and other means.
8. Interpreting the data to discover solutions and opportunities.
A data scientist is someone who makes value out of data.. Data scientist duties typically include creating various machine learning based tools or processes within the company such as recommendation engines or automated lead scoring systems.
4 types of data science jobs
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward.
3. The Machine Learning Engineer
There are a number of companies for whom their data is their product. In this case, the data analysis or machine learning going on can be pretty intense.
4. The data science generalist: A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company.
1. IT Systems Analyst
Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.
2. Healthcare Data Analyst
Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.
The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs. Plus, with a rise in regulations and restrictions on how that data can be stored, retrieved, and processed, demand for proficient data analysts is on the rise as well.
3. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants.
Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military and can make upwards of $75,000 annually. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.
4. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries.
Data Science is a field that uses scientific methods, processes and systems to extract knowledge and insights from various structural and unstructured data.
Data Science is considered as an entirely new career field.
Data Science is related to Data Mining and Big Data.
Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Skills that are required by a person working in that Data Science field are as follows:-
1) Programming Language Skills i.e. Python & SQL
2) Data Visualization or Analytical Skills
3) Statistical Knowledge
The Data Science Jobs Available in the Current Market are as follows:-
1) Data Analyst
2) Data Scientist
3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data.
A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills.
Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
The Various Other Posts Available are:- Machine Learning Scientists, Business Intelligent Analyst, Statistician, Quantitative Analyst & Data Warehouse Architect.
Data Science is a field that uses scientific methods, processes and systems to extract knowledge and insights from various structural and unstructured data.
Data Science is considered as an entirely new career field.
Data Science is related to Data Mining and Big Data.
Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Skills that are required by a person working in that Data Science field are as follows:-
1) Programming Language Skills i.e. Python & SQL
2) Data Visualization or Analytical Skills
3) Statistical Knowledge
The Data Science Jobs Available in the Current Market are as follows:-
1) Data Analyst
2) Data Scientist
3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data.
A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills.
Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
The Various Other Posts Available are:- Machine Learning Scientists, Business Intelligent Analyst, Statistician, Quantitative Analyst & Data Warehouse Architect.
1) OPERATION ANALYST
Operation analyst work as a consultant and even work internally in large organisation.His main focus is on internal processes of a business like internal reporting system, product manufacturing and distribution, and the general streamlining of business operations.
They have a technical knowledge of the systems they are working with and it’s more important for professionals.
2) DATA ANALYST
Data scientists collect and analyze data and communicate actionable insights.They are able to understand data from a more informed perspective. These positions require a strong knowledge of data analytics like software tools, programming languages like Python or R, and data visualization skills etc
3) DATA ENGINEER
Data engineers focus more on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.As they are the high-level data analytics professionals they need to upgrade a database infrastructure for faster queries.
4) QUANTITATIVE ANALYST
A quantitative analyst is another data science job, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems.
They may also venture out on their own creating trading models to predict the prices of stocks, commodities, exchange rates, etc.
5) DATA ANALYST CONSULTANT
Data analytics consultant is to deliver insights to a company to help their business. The difference between a consultant and data analyst is that a consultant may work for different companies in a shorter period of time and also for more than 1 company at a time.
6) TRANSPORTATION LOGISTICS SPECIALIST
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies like Amazon, UPS, airlinea etc.
They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.They also need to identify the most efficient paths for products and services to be delivered.
7) PROJECT MANAGER
Project managers is a person who use analytics tools to keep track of a teams progress,track their efficiency, and increase productivity by changing processes.Project managers need at least a working understanding of data analytics, and often more.These positions are usually found internally at large corporations and in management consulting too.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathem4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.atics, statistics, or physics background and is hoping to continue
down a more academic path.
5.Data Architect:Here he/she has to ensure that data solutions are built for performance and design analytics applications for multiple platforms.
Companies: IBM, eBay, AAA Club Alliance, T-Mobile
Data scientists :
data scientists responible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Data Science Career Opportunities :
A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. Therefore it comes as no surprise that Data Scientists are coveted professionals in the Big Data Analytics and IT industry.
Data Scientist Salary Trends:
A report by Glassdoor shows that Data scientists lead the pack for the best jobs in America. The report goes on to say that the median salary for a Data Scientist is an impressive $91,470 in the US and ₹622,162 and there are over 2300 job openings posted on the site
In India the trend is no different; as of May 2019, the median salary for a Data Scientist role is Rs. 622,162 according to Payscale.com.
Data Scientist Job Roles:
A Data Scientist dons many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Some of the prominent Data Scientist job titles are:
• Data Scientist
• Data Architect
• Data Administrator
• Data Analyst
• Business Analys
Data science is considered as a new career field in various platforms like Industry, tools and operations etc companies are bringing more data oriented personnel and for good reason
As a data scientist they are prevalent in Knowing how shifting will be done in near feature
The current trends which ensured to except data science are:
1)Automation:
This is a huge one especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
2) Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
3)Ethics and Influence:
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers
4)Blockchain App Development:
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well namely because of the implications blockchain has to a great many industries.
Each of these markets requires its own strategy sourcing pools of experienced data scientists and analysts for one, and employee development for the other
Thus data science jobs available in various current market has helped in emerging discipline at various levels of academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
1.MIS Reporting Executive.
2.Business Analyst.
3.Data Analyst.
4.Statistician.
5.Data scientist.
6.Data Engineer.
7.Data Architect.
8.Machine Learning Engineer.
9.Big Data Engineer.
Data Science-Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.
India is expected to see 1.5 lakh new openings in the emerging and in-demand field of data science in 2020 -- an increase of around 62% compared to 2019.
Data Analyst- A data analyst collects and stores data on sales numbers, market research, logistics, linguistics, or other behaviors. They bring technical expertise to ensure the quality and accuracy of that data, then process, design and present it in ways to help people, businesses, and organizations make better decisions.
Data Engineers- Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages. But data engineers also need communication skills to work across departments to understand what business leaders want to gain from the company’s large datasets.
Business Intelligence Developer - The general role of a business intelligence developer is to create and manage an organization’s business intelligence and analytics solutions, allowing companies to make better and more informed decisions. A business intelligence developer is responsible for designing and developing BI solutions, overseeing the implementation of the BI systems and also the ongoing maintenance of those solutions. They will create and complete data request queries and, through visualization and reports, present the information.
Quantum computing- Quantum computers are known to perform complex calculations in supersonic speed, completing the computational tasks in a few seconds. The quantum bits (QuBits) can store a large volume of data and run complex computations in split seconds. Companies like Intel, Google, and IBM, are going to be the pioneers of this industry, but they are yet in their humble beginnings.
Automated Machine Learning- AutomML is growing in prominence. It is an automation layer on top of machine learning, boosting the development efficiency and overall productivity of data scientists. The data science tasks like data preparation, choosing data sources, and feature selection are repetitive and monotonous. AutoML looks into automating these tasks. AutoML can also perform quality modelling and fine-tuning of artificial intelligence (AI) and machine learning algorithms. Giants like Facebook and Google are already using AutoML for their internal data science processes.
Data science is the study of data.It involves developing methods.It is a concept to unify statistics data analysis.
Data science job involves the following steps or process.
1.Business problem -
Firstly need to understand the problems which need to be solved.
2.Data acquisition -
After identifying the problem.Gathers information through web series,logs,databases, API's, online respositories.
3.Data preparation -
This step involves data cleaning
And transformation.It is time consuming as it is handling many complex scenario.
4.Exploratory data analysis-
It defines Nd refines selection of feature variables that will be used in model development.
5.Data modeling -
Here we can apply diverse machinery technique like KNN, Decision tree, Naive Bayes to the data.To identify the best suitable the business requirements and test them to select best performing.
6. Visualization and communication -
After going through the above procedure need to prepare reports Nd recommendations which helps to present and commucates .
7.Deploys and maintain -
Test the selected model in pre-marketing before using it in production system and uses the prepared report to maintain performance.
Data science involves various disciplines, including statistics, data analysis, machine learning, and computer science. Different companies have their different roles some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. Data scientist is often used as a blanket title to describe jobs that are drastically different. Let’s looks at four kind of data science jobs.
The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.once if this can be handled on day today basis company will have great environment to apply the new things.
The Data Engineer
Some have to handle lot of data and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward.they also look for analyst Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
The Machine Learning
There are a number of companies for whom their data is their product.In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
these companies are either looking for generalists or they’re looking to fill a specific niche where they feel their team is lacking, such as data visualization or machine learning.
Different companies are seeking different skillsets, expertise, and experience levels. As you look for your ideal data scientist job, make sure to look closely at the job descriptions, to find the role and company that best match your skills and experience
Each of these types of data scientists have different skills that matter most.
1 Statistician: the field of statistics has always been about number crunching.a strong statistical base qualifies you to explore your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, analysis of variance, data visualization are some of the skills possessed by statisticians
2 Data scientist as mathematician: Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics.
3 Data engineers: These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements.
4 Machine learning scientists:machine learning scientists are the programmers who develop machines and systems that can learn and apply knowledge without specific direction.artificial intelligence is the goal of a machine learning engineer.
5 Digital analytic consultant: This is a very popular position and a number of organizations – ranging from Fortune 500 to small non – for – profits – seek digital analytics talent. It is a common misconception that a digital analytic professional only needs technical talent. In addition, one also needs to be sound in business and marketing skills to be successful.
6 Quality Analyst: Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards.
These are the some data scientists or the data engineer jobs available in the current market.They identify the problems that arises in the organisation and also determine the correct data sets and variables.they also collect the structured and unstructured data from disparate sources.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways.
Data science isn't exactly IT. It requires fewer number of people, but the expected productivity has to be very good. Whether we're making a ML product or solving business problems such as price points at which I should sale, it requires a few people to use their brain on varied work than a set of redundant work that IT normally does. Think of data science as the most advanced and coding and mathematics heavy business analyst.
Data science jobs available in the current market are as follows:
1. The Data Analyst :
A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. “Data analysts' work varies depending on the type of data that they're working with (sales, social media, inventory, etc.)
2.The Data Engineer :
Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists. They are software engineers who design, build, integrate data from various resources, and manage big data.
3. The Machine Learning Engineer :
Machine learning engineer do one of the toughest job to create a ML or AI-based model that can work properly with best performance.
4.The Data Science Generalist :
Data scientists analyze data, including data on customer behavior and preferences, to help businesses decide what direction they should be taking next. However, not every piece of data that a company gathers is clear or useful information.
5.Operation analyst :
Operation analyst work as a consultant and even work internally in large organisation.His main focus is on internal processes of a business like internal reporting system, product manufacturing and distribution, and the general streamlining of business operations.
Data Science scope is very bright.there are lot of companies which hire Data Scienctists:
1. Startups: Ola cabs, Uber, Housing...
2. ECommerce: Snap deal, Flip-kart, Amazon...
3.Product based MNCs: Microsoft, VMware, Honeywell...
4.Service based analytics company: Mu-Sigma, Vizury, Fractal Analytics, Impetus, TCS...
There are many job openings for data scientists in private enterprises, public domain as well as in bigger corporations.
1.Data Scientist
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance data scientists are no longer just success stories for global giants such as Google, LinkedIn, and Facebook.
Roles & Responsibilities:
“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business
2.Data Engineer/Data Architect
Data engineers are the designers, builders and managers of the information or big data infrastructure.Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Roles & Responsibilities:
Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path.
3.Machine Learning Engineer
Machine learning has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
Roles & Responsibilities:
Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.
4.Big Data Engineer
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Roles & Responsibilities:
“Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive, MongoDB or Cassandra.
1. IT Systems Analyst
Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests.
2. Healthcare Data Analyst
Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.
The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs.
3. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants.
Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
4. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
6. Quantitative Analyst
A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc.
7. Data Analytics Consultant
Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.
They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.
8. Digital Marketing Manager
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset.
Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
9. Project Manager
Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.
Project managers need at least a working understanding of data analytics, and often more.
These positions are found internally at large corporations, and frequently in management consulting.
10. Transportation Logistics Specialist
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon or an supply chain organization.
1.Data Scientist
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance data scientists are no longer just success stories for global giants such as Google, LinkedIn, and Facebook.
Roles & Responsibilities:
“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business
2.Data Engineer/Data Architect
Data engineers are the designers, builders and managers of the information or big data infrastructure.Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Roles & Responsibilities:
Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path.
3.Machine Learning Engineer
Machine learning has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
Roles & Responsibilities:
Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.
4.Big Data Engineer
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Roles & Responsibilities:
“Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive, MongoDB or Cassandra.
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
vijay ganji 2GZ19MBA46
List of Job Roles in Data Science in current market
1. MIS Reporting Executive
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
2. Business Analyst
Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
3. Data Analyst
Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
4. Statistician
Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.
6. Data Engineer/Data Architect
“Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer
Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data
8. Big Data Engineer
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
According to the sources a market study gives a view of the changes in market and highlights how employees need to develop skills in data science. The reports clearly demonstrates while skills in Python and Java are highly sought after, a professional programmer or the data analyst should hav the ability to learn new coding language.
The trends in data science jobs are as follows :
Job Openings in Data science sector: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
2) Forecasting annual growth in data science professionals : Compared to the numbers in 2017, in 2019 year there has been an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared t0 previous years .
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India.
Data scientist is the most demanded job in 21st century.with the surge and is correlate fields, the job of a data scientist has become the most sought after. Many IT professionals and academicians who have worked in quantitative fields want to become data scientists. The demand for data scientist is only increasing and will continue to increase in future
Data science over the next few decades: it is predicted to grow over the next. It is a staggering fact that over 90% of the data in the world was generated on just 2 years
It is rather an undefined and cured term. It is general term that has several definition with the passage of time, the data science roles will become more concretized. There will be a concise defination that will imparted to data science that will enable the scientist to handle corresponding operation.deeper career paths will be developed in data science.there is a diversification of role of data scientist. The rise in demand for data scientists will prompt educational institutes to include it in there curriculum. The data literacy will increase in future
General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data.
Quantitative analyst -
quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
Data architect:
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture.
Machine learning scientists: They research new data approach and algorithms
Companies: Apple, Tinder
Data Analyst:-
A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data Scientist:-
A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
Data Engineer:-
A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
Data science includes statistics, data analysis, machine learning, and computer science.
Companies provide jobs depending on the experience levels of the candidates.
There are different types of data science jobs available in the market. Such as,
*Data analyst:
Data analyst is the one who performs tasks like pulling data out of SQL databases, operating excel and tableau, producing basic data visualisations and reporting. They take the lead on the company's Google Analytics account.
*Data Engineer:
Data Engineer is the one who performs tasks like controlling the data traffic of the company when there is huge dat traffic. They set up the data infrastructure in order to keep the company moving forward.
*Machine learning engineer:
There are no.of companies for whom their data is their product. So in that case, the data analysis or machine learning is intense. This is the ideal situation for someone who has formal maths,stats and physics background. They often focus on producing data-driven products.
*Data science generalist:
Data science generalist joins the established team of data scientists.
They perform analysis, touch production code, visualise data,etc.
1. Data Analyst:
A data analyst's primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data analyst is someone who collects, processes and performs statistical analysis of data also a data analyst can translate numbers and data into plain English in order to help organization and company to make better business decisions.
2. Data scientist:
A data scientist is a person who is employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making. A data scientist can have more freedom to pursue their own ideas and experiment to find intresting patterns and trends in the data.
3. Data engineer:
A data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large scale processing systems. They need to recommend and sometimes implement ways to improve data reliability, efficiency and quality.
They are also responsible for building and maintaining infrastructure needed to store and quickly access past data.
4. Machine learning engineer:
It means a data scientist who has specialized in machine learning. A machine learning engineer is more of a software engineering role that involves taking a data scientists analysis and turning it into deployable software. They are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
5. Quantitative analyst:
They are also called as quants. They use advanced statistical analyses to answer questions and make predictions related to finance and risk. A person who analyses a situation or event, especially a financial market, by means of complex mathematical and statistical modelling.
6. Business intelligence analyst:
It is a professional role where the individual is responsible for analysing data that is used by a business or organization. They generally support decison making. A BI analyst must be focused on analysing market and business trends.
7. Statistician:
They are generally data scientists before it existed.
8. Business analyst:
They help companies answer questions and solve problems. They require analyst to capture, analyze and make recommendations based on company's data.
9. System analyst:
They are often tasked with identifying organisational problems and then planning and overseeing the changes or new systems required to solve those problems.
Vishal kurade 2GZ19MBA53
The Data Scientist
1) Data Scientist is most likely one of the most sizzling job titles that you can have these days
2) The Data Analyst
Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity
3)The Database Administrator
As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save
4)Business analyst
A business analyst therefore often performs the role of the middle person between the business folks and the techies.
5)Qualitative expert
they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker.
Data analyst-
This is typically considered an entry level position in the data science field, although not all data analysts are junior and salaries can range widely.
A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.
Business Analyst
Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest terms, a business analyst helps companies answer questions and solve problems. This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them. But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make you a more compelling candidate for almost any business analyst role.
Systems Analyst
Systems analysts are often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems. This typically requires programming skill (although systems analysts are not always directly involved in developing the systems they recommend) and data analysis and statistical skills are also frequently necessary for identifying problematic trends and quantifying what’s working well and what isn’t within a company’s tech systems.
Marketing Analyst
Marketing analysts look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. In the digital age, these analysts have access to increasingly large amounts of data, particularly at companies that sell digital products, and while there are a variety of software solutions like Google Analytics that can allow for decent analysis without programming skills, an applicant with data science and statistics chops is likely to have a leg up on many other applicants if they also have sufficient domain knowledge in the area of marketing. Plus, a marketing analyst whose analyses make a significant impact can set their long-term sights on a Chief Marketing officer.
1. Data Analyst:
A data analyst's primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data analyst is someone who collects, processes and performs statistical analysis of data also a data analyst can translate numbers and data into plain English in order to help organization and company to make better business decisions.
2. Data scientist:
A data scientist is a person who is employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making. A data scientist can have more freedom to pursue their own ideas and experiment to find intresting patterns and trends in the data.
3. Data engineer:
A data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large scale processing systems. They need to recommend and sometimes implement ways to improve data reliability, efficiency and quality.
They are also responsible for building and maintaining infrastructure needed to store and quickly access past data.
4. Machine learning engineer:
It means a data scientist who has specialized in machine learning. A machine learning engineer is more of a software engineering role that involves taking a data scientists analysis and turning it into deployable software. They are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction.
5. Quantitative analyst:
They are also called as quants. They use advanced statistical analyses to answer questions and make predictions related to finance and risk. A person who analyses a situation or event, especially a financial market, by means of complex mathematical and statistical modelling.
6. Business intelligence analyst:
It is a professional role where the individual is responsible for analysing data that is used by a business or organization. They generally support decison making. A BI analyst must be focused on analysing market and business trends.
7. Statistician:
They are generally data scientists before it existed.
8. Business analyst:
They help companies answer questions and solve problems. They require analyst to capture, analyze and make recommendations based on company's data.
9. System analyst:
They are often tasked with identifying organisational problems and then planning and overseeing the changes or new systems required to solve those problems.
Data Science
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.
Data Science is considered as an entirely new career field.
Data Science is related to Data Mining and Big Data.
Data Science employs techniques and theories which is drawn from many fields such as; Mathematics, Statistics, Computer Science and Information Science.
The Data Science Jobs Available in the Current Market are as follows:-
1) Data Analyst
2) Data Scientist
3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data.
A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills.
Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format.
Role and resposibility of An Data scientist
-Work with stakeholders to determine how to use business data for valuable business solutions
-Search for ways to get new data sources and assess their accuracy
-Browse and analyze enterprise databases to simplify and improve product development, marketing techniques, and business processes
-Create custom data models and algorithms
skills required
-A natural inclination toward solving complex problems
-Knowledge/experience on/with statistical programming languages, including R, Python, SLQ, etc., to process data and gain insights from it
-Experience using and developing data architectures
-Knowledge of Machine Learning techniques, including decision tree learning, clustering, artificial neural networks, etc., and their pros and cons
-Knowledge and application experience in advanced statistical techniques and concepts, including, regression, distribution properties, statistical testing, etc.
-Good communication skills to promote cross-team collaboration
-Impulse to learn and master new technologies
-Multilingual coding knowledge/experience: Java, JavaScript, C, C++, etc.
-Experience/knowledge in statistics and data mining techniques, including, random forest, GLM/regression, social network analysis, text mining, etc.
----AKASH S PATIL
2GI19MBA06
Data analyst-
This is typically considered an entry level position in the data science field, although not all data analysts are junior and salaries can range widely.
A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.
Business Analyst
Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest terms, a business analyst helps companies answer questions and solve problems. This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them. But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make you a more compelling candidate for almost any business analyst role.
Systems Analyst
Systems analysts are often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems. This typically requires programming skill (although systems analysts are not always directly involved in developing the systems they recommend) and data analysis and statistical skills are also frequently necessary for identifying problematic trends and quantifying what’s working well and what isn’t within a company’s tech systems.
Marketing Analyst
Marketing analysts look at sales and marketing data to assess and improve the effectiveness of marketing campaigns. In the digital age, these analysts have access to increasingly large amounts of data, particularly at companies that sell digital products, and while there are a variety of software solutions like Google Analytics that can allow for decent analysis without programming skills, an applicant with data science and statistics chops is likely to have a leg up on many other applicants if they also have sufficient domain knowledge in the area of marketing. Plus, a marketing analyst whose analyses make a significant impact can set their long-term sights on a Chief Marketing officer.
1. IT Systems analysts: System analyst use and design systems to solve problems in information technology.Some system analyst use existing third party tools to test software within a company.
2. Health care data analyst: Health care data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientist find answers to the questions and problems they encounter on a daily basis.
3.Operational analyst: operations analysts are usually found internally at large companies but may work as consultants. they focus on the internal processes of a business. This can include internal reporting systems,product manufacturing and distribution and the general streaming of business operations.
4. Data Scientist: much like analysts in other roles, data scientist collect and analyze data and communicate actionable insights. Data scientists understand data from a more informed perspective to help make predictions.
5. Data Engineer: Data engineers often focuses on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes. They also need to upgrade a database infrastructure for faster queries.
6.Quantitative analysts: This is another highly sought after professional, especially in financial firms. Quantitative analysts use data analytical to seek out potential financial investment opportunities or risk management problems.
7. Digital marketing manager: Digital marketing requires a strong knowledge of data analytics. Marketers often use tool like google analytics, custom reporting tools and other third party sites to analyse traffic from websites and social media advertisements.
8. Project manager: These managers use analytics tool to keep track of a team's progress, track their efficiency and increase productivity by changing processes. Project manager need at least a working understanding of data analytics and often more.
9. Transportation logistics specialist: A transportation logistics specialist optimises transportation of physical goods and could be found in large hipping companies. The transportation logistics specialist need to reliably identify the most efficient paths for products and services to be delivered.
General Manager : They provide market, market share and competitor information and analysis for the strategy process to grow the business and identify new opportunity areas.And also to create actionable insights by connecting to other digital data.
Quantitative analyst -
quantitative analyst sometimes called points use advanced statistical analysis to answer questions and make prediction related to finance and risk.needless to say, most data science programming skills are immensely useful for quantitative analysis and a solid knowledge of statistics is fundamental to the field.understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
Data architect:
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture.
Machine learning scientists: They research new data approach and algorithms
Companies: Apple, Tinder
Data Analyst:-
A Data Analysts primary job is to look after company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
Data Scientist:-
A Data Scientist do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A Scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends.
Data Engineer:-
A Data Engineer manages a company's data infrastructure. Their job requires a lot of statistical analysis and a lot more software development and programming skills.
Data Scientist skills are in demand that will fetch big career opportunities in Data Science. These were some of the jobs available in the field of Data Science and the job description.
1.Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average for B.A.+ jobs of 45 days.1 Requirements for data science and analytics jobs are often multidisciplinary and they all require an ability to link analytics to creating value for the organization. The analytics and technology skills vary widely, but candidates must also demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork. This holistic skill set is rare, so you should expect to compete fiercely for T-shaped individuals, as they are now often called, meaning those with a principle competency, plus well-honed broad skills to help them cross functions or domains.
2.In essence, there are two different markets for data science and analytics jobs. Across the ecosystem, we see two broad families: analytics-enabled jobs and data science jobs.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data.
Various data science jobs available in the current markets.
1) Data Analyst-B2B Marketing
The B2B Marketing data analyst will spend most of their time analyzing data that includes past trends, current conditions, projected etc.
This job requires a strong candidate with strong mindset with problem solving skill, technical acumen, and ability to solve the business problems.
Analysis of data to provide assessments of trends, to identify and explain variances from goals, between periods and across teams to identify possible performance issues or challenges.
2) Data Insights and Analyst
This job requires prior experience in business analytics and knowledge of related analysis or visualization tools.
Expecting a minimum of 2-4 years of relevant experience.
3) Machine learning
Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Responsibility is to build and manage actionable sales Analytics solution and KPIs to continually improve process and develop analyticanalytics products and manage the featurefeature roadmap for analytical components.
4) Sales Planning Analysts
The position is responsible to support the global sales organization by assisting development of
Strategic plans and Business Process using data science and Business intelligence technologies.
5) Digital Data Analytics
Digital analytics is the process of analysing digital data from various sources like website, mobile application etc.
It is tool usedby organization for collecting, measuring, and analyzing the qualitative and quantitative data.
6) Product Manager
The product manager is responsible for defining the why, when and what itmf the product that engineering team builds.
The Product Manager role is further complicated by how much decision-making power the product management function gets in the organization.
Mitali B Nevagi 2GI19MBA53
Data science process in collecting, storing, segregating and analyzing data which serves as a valuable resources to the organisation to carry out data and take important decisions they use highly skilled computer professionals.
Big data analysis is the best career today to choose.
They help the organisation to deal with costs, increases efficiency and also recognize new market opportunities.
Data scientists are highly educated and boast of intelligence and a certain skill set relevant to the field.
Some of the data science jobs are:
Business intelligence developer
Data architect
Infrastructure architect
Enterprise architect
Data analyst
Date engineer
Machine learning scientist
Statistician
Data science experts are requ and valued in almost every field today. Many businesses and government depend on big data to provide effective services.
Data science is the field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining.
Data scientists specific task include following:
•Identifying the data-analytics problems that offer the greatest opportunities to the organization.
•Determining the correct data sets and variables.
•Collecting large sets of structured and unstructured data from disparate sources.
•Cleaning and validating the data to ensure accuracy, completeness, and uniformity.
•Devising and applying models and algorithms to mine the stores of big data.
•Analyzing the data to identify patterns and trends.
•Interpreting the data to discover solutions and opportunities.
•Communicating findings to stakeholders using visualization and other means.
Data science helps in building better future for both an individual and companies.
Some of the data science jobs are:
1. Business Intelligence Developer
BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems.
2. Data Architect
Ensure data solutions are built for performance and design analytics applications for multiple platforms.
3. Data Architect
Ensure data solutions are built for performance and design analytics applications for multiple platforms.
4. Data Analyst
Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.
5. Data Engineer
Perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists.
6. Machine Learning Scientist
Research new data approaches and algorithms.Create data funnels and deliver software solutions.
7. IT Systems Analyst
Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests.
Data science benefits both companies and consumers alike.
Preeti. N. Shinde
2GZ19MBA18
1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual.
2) Forecasting annual growth in data science professionals in 2018: Compared to the numbers in 2017, last year had an optimistic job growth with a 45% increase in open job requirements
3) Increase in analytics jobs offering more than 15 lakh per annum:There has been an 2% increase in the numbers of analytics jobs offering more than 15 Lakh annual salary as compared to 2017.
4) Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom
5) Python will continue to dominate the market: Python continues to be the tool of choice among data analysts and data scientists and this is reflected in the hiring market as well with 17% jobs listing the language as a core capability
6) Talent hotspots in India: As per our research, Bengaluru leads the jobs market with a mature analytics ecosystem accounting for 24% of analytics jobs in India. The other hubs are Delhi/NCR and Mumbai market and in addition to this, data science and analytics markets are also forming in Tier-B cities
7) Hiring trend indicates demand for junior level talent rises: According to our estimate, as compared to previous year, the hiring trend has been more favourable for young talent with 21% jobs being posted for freshers
Highlights
While, it is difficult to ascertain the exact number of open analytics job openings; according to our estimates, close to 97,000 positions related to analytics & data science are currently available to be filled in India.
This is almost 45% jump in the open job requirements, compared to same time a year back.
Compared to worldwide estimates, India contributes 6% of open job openings currently. Growth in the number of data science jobs globally was much higher than India
Last year India contributed 10% of worldwide open job requirements which has decreased to 6% this year, even though there have been an overall growth in numbers
10 leading organisations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte
Almost 97% of analytics jobs advertised in India are of full-time basis. Just 3% form the part-time, internship or contractual jobs
Top designations advertised are: Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant, Statistical Analyst, Hadoop Developer
Data science experts are needed in virtually every job sector—not just in technology. Here are some of the leading data science careers you can break into with an advanced degree.
1. Business Intelligence (BI) Developer:
BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems
Notable Companies: DollarShave Club, Discover, and Liberty Mutual
2. Data Architect:
The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms.
Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Applications Architect:
The job is to track the behavior of applications used within a business and how they interact with each other and with users.
Notable Companies: UPS, Humana, Dow Jones, Oracle
4. Infrastructure Architect:
They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.
Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
5. Data Scientist:
They Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.
Notable Companies: Facebook, Capital One, Airbnb, Twitter
6. Data Analyst:
The data analyst transforms and manipulates large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.
Notable Companies: Walmart, Gap, Bank of America, Kohler.
Data science is most demanded skill in the market today both, in India as well al globally.the scope of Data science is increasing day by day.
Data analysis jobs hepls the following:
-data engineer: he is the person who manages company's data infrastructure. This includes lot of statistics, calculations.
-data architect: he manages the data related to the architecture, the design maps, the layouts etc..
According to data science institution experts, the data science market is to grow in size to at least one-third of the IT. It says the the demand and the supply of the data analyst are not according to the expected number.
Around 70% of the data science job openings were for candidates with less than five years of experience, of which 21% were for pure freshers. Another 31% of open positions were offered to professionals with more than five years of industry work experience. The high demand for a data science professional has got most software engineers reskilling them to cater to the needs of the data science industry.
Data analysis plays an important role in the market as it helps the company, thr economy to collect the various data.
Data analysis deals with he major calculations of various data in terms of statistics, algorithms etc..
Data analyst: converts the numerical data into an understanding data is in a specific language, where it helps the company to know exact status of their product. Like how many people has the product attracted, or been sold, or been purchased.
This will help the company to make further decision for the company but analysing the above facts as driven by the data analyst.
Data science job opportunities in future market:
1 Data warehouse architect:
This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst:
The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician:
statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst :
marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
1.MIS Reporting Executive.
2.Business Analyst.
3.Data Analyst.
4.Statistician.
5.Data scientist.
6.Data Engineer.
7.Data Architect.
8.Machine Learning Engineer.
9.Big Data Engineer.
Data science job opportunities in future market:
1 Data warehouse architect:
This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst:
The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician:
statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst :
marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
Data science experts are needed in virtually every job sector—not just in technology. Here are some of the leading data science careers you can break into with an advanced degree.
1. Business Intelligence (BI) Developer:
BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems
Notable Companies: DollarShave Club, Discover, and Liberty Mutual
2. Data Architect:
The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms.
Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3. Applications Architect:
The job is to track the behavior of applications used within a business and how they interact with each other and with users.
Notable Companies: UPS, Humana, Dow Jones, Oracle
4. Infrastructure Architect:
They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.
Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
5. Data Scientist:
They Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.
Notable Companies: Facebook, Capital One, Airbnb, Twitter
6. Data Analyst:
The data analyst transforms and manipulates large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.
Notable Companies: Walmart, Gap, Bank of America, Kohler.
1) Data Scientist as Statistician
This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields.Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2) Data Scientist as Mathematician
Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientists Vs Data Engineers
These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. Additionally, he is also responsible for its smooth functioning. They need to work closely with data scientists, IT managers and other business leaders to translate raw data into actionable insights which would result in competitive edge for the organization.
4) Data Scientist as Machine Learning Scientists
Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).
5) Data Scientist as Business Analytic Practitioners
Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts. They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc.
Pavitra Hubballi
B Division
Usn: 2GZ19MBA10
Data science job opportunities in future market:
1 Data warehouse architect:
This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2. Data Architect:
The job of data architect is they Ensure data solutions are built for performance and design analytics applications for multiple platforms.
Notable Companies: IBM, eBay, AAA Club Alliance, T-Mobile
3 statistician:
statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4. Infrastructure Architect:
They Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.
Notable Companies: Abbott Labs, Hewlett-Packard, Dell, Ford Motor Company
Gurunath V. Shiroorkar
2GI19MBA31
1.Business Intelligence (BI) Developer: A business intelligence developer is responsible for designing and developing BI solutions, overseeing the implementation of the BI system and also the ongoing maintenance of those solutions.
2. Data Architect : A data architect is a practitioner of data architecture ,a data management discipline concerned with designing, creation, deploying and managing an organizations data architecture.
3. Applications Architect : application architecture or application architecture is one of several architecture domains that form the pillars of enterprise architecture.
4. Infrastructure Architect: Infrastructure Architects design and implement information systems to support the enterprise infrastructure of an organization. They ensure that all systems are working at optimal levels and support the development of new technologies and system requirements.
5. Data Analyst: A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.
6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists.
Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average. The following are the list of job data science are:
1. Business analyst- business analyst are experts in the domain they work in.they have the narrow gap between the business and information technology.business analyst provides solutions that are often technology.
2.Data analyst- data analyst are more of generalist.they play a measure role from acquiring Massive amounts of data to processing and summarising it.
3. Data engineer or data architect- data enginner are the designers,builders and managers of the information or big data infrastructure.data engineers ensure that an organisations big ecosystem.
4. Machine learning engineer- Machine learning has become quite a booming field with the mind boggling amount of data.engineers should focus on python,java,c++ etc.
5. MIS reporting executive- business manager rely on management information system reports to automatically track progress,make decisions and indentify problems.
6. Data scientist- this is one of the most in demand professionals today data scientist rule roost of number.data scientist are no longer they were scripting success stories are Google, LinkedIn and Facebook.
7. Big data engineer - big data solutions architect designs,big data engineer builds.big data is a big domain every kind of role has its own specific responsibilities.
Shreya Sainuche
2GZ19MBA34
List of Job Roles in Data Science / Big Data
1. MIS Reporting Executive
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
2. Business Analyst
Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
3. Data Analyst
Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it
4. Statistician
Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.
Almost every company has some sort of a data role on its careers page. Job Descriptions for data scientists and data analysts show a significant overlap.
6. Data Engineer/Data Architect
“Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer
Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
8. Big Data Engineer
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Descriptive analytics
Descriptive analytics answers the question of what happened. Let us bring an example from ScienceSoft’s practice: having analyzed monthly revenue and income per product group, and the total quantity of metal parts produced per month, a manufacturer was able to answer a series of ‘what happened’ questions and decide on focus product categories.
Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, our data consultants don’t recommend highly data-driven companies to settle for descriptive analytics only, they’d rather combine it with other types of data analytics.
Diagnostic analytics
At this stage, historical data can be measured against other data to answer the question of why something happened. For example, you can check ScienceSoft’s BI demo to see how a retailer can drill the sales and gross profit down to categories to find out why they missed their net profit target. Another flashback to our data analytics projects: in the healthcare industry, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) allowed identifying the influence of medications.
Diagnostic analytics gives in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal, otherwise, data collection may turn out to be individual for every issue and time-consuming.
Predictive analytics
Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Check ScienceSoft’s case study to get details on how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
Predictive analytics belongs to advanced analytics types and brings many advantages like sophisticated analysis based on machine or deep learning and proactive approach that predictions enable. However, our data consultants state it clearly: forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires careful treatment and continuous optimization.
Prescriptive analytics
The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from our project portfolio: a multinational company was able to identify opportunities for repeat purchases based on customer analytics and sales history.
Prescriptive analytics uses advanced tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage. Besides, this state-of-the-art type of data analytics requires not only historical internal data but also external information due to the nature of algorithms it’s based on. That is why, before deciding to adopt prescriptive analytics, ScienceSoft strongly recommends weighing the required efforts against an expected added value.
Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further.
Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreak hit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic.
Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.
In a 2015 speech, Economist and Freakonomics author Steven Levitt said that CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization […] is absolutely going to be at the center of what economics is and what other social sciences are going forward.”
Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.
Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further.
Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreak hit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic.
Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.
In a 2015 speech, Economist and Freakonomics author Steven Levitt said that CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization […] is absolutely going to be at the center of what economics is and what other social sciences are going forward.”
Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.
Information or data is the new oil, and there is an ever increasing demand for
millions of professionals across the world to drill into that data, clean it, refine it, infer
insights and sell it to the world at a premium. Hence, there is a huge demand for analytics professionals who understand this domain. By combining the power of science, art, and technology.
Data science jobs available in the current market are as follows:
1.Data analyst :Analytics is broadly defined as the systematic computational analysis of data or
statistics and analysts lie at the confluence of three streams which are data science
skills, industry/domain understanding, and technology advancements.
2.Data scientist:The job of the
data scientist is the same as that a statistician’s role, incorporating the use of
advanced analytics and leading to accurate predictions which are essential for
business.
3. Data Engineer:Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages.
4. Machine learning engineer:Machine Learning is a technical tool of data science that creates logic from data by transforming data into knowledge.
It provides efficient analysis models for capturing knowledge by
improving prediction for data driven decisions. It plays an eminent role in the field of computer
science that paves its way in analyzing robust emails, spam filters, convenient text, voice
recognition, web search engines, game developments and self – driving cars.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
4 Types of Data Science Jobs
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google .
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
“Mentorship opportunities for junior data scientists can be less plentiful at a company looking to leverage rapidly increasing amounts of data.”
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
Data Science Job Market Is Booming
Analytics professionals command higher compensation and outearn their peers considerably. As per data from our Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Furthermore, this year, the entry level professionals ((0-3 years experience) saw a slight increase in salaries, with compensation going up from ₹5.2L median last year to ₹5.3L per annum. At the entry level, almost 76% of analytics professionals earn under 6 lakh per annum.
Enterprises Double Down on Analytics Initiatives
As organisations continue to widen their analytics efforts, there is a rising demand for analytics professionals who work at the intersection of data and business, can prioritise business problems, and can help organisations achieve real impact from their data-driven initiatives.
Here’s What the Industry Demands
Strong educational background: In order to work with agile teams, organisations look out for candidates with a Bachelor’s degree or MS in quantitative fields like engineering, Math, Statistics and more popularly CS. In most cases, an MS is a huge advantage.
Tool-chain: Knowledge of programming like Python and R is a must to break into the data science field. In addition to this, one should also have proficiency in statistical packages such as SAS or SPSS that are also used in organisations.
Experience working with large datasets: As data grows by leaps and bounds, enterprises are dealing with both structured and unstructured data. Our study confirms that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Domain knowledge: Data analytics professionals have to work at the intersection of business and technology and drive result-driven projects. One of the key aspects is to understand key operational metrics and the impact on business. In addition, knowledge of common use cases such as churn prediction (telecom), customer acquisition (e-marketplaces), inventory management (e-marketplaces), predictive maintenance (manufacturing) can be a plus.
1) Data Scientist is most likely one of the most sizzling job titles that you can have these days
2) The Data Analyst
Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity
3)The Database Administrator
As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save
4)Business analyst
A business analyst therefore often performs the role of the middle person between the business folks and the techies.
5)Qualitative expert
they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker.
Sairaj Aihole
USN 2GZ19MBA27
. MIS Reporting Executive
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization
2. Business Analyst
Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity
3. Data Analyst
Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
In our annual study with Great Learning, we examine the landscape of job requiring data science and analytics competencies and skills. Our market snapshot gives a quick view of the evolving talent market and highlights how employees need to develop a blend of skills to strive ahead in data science roles. The report clearly demonstrates while skills in Python and Java are highly sought-after, a professional programmer or a data analyst should have the ability to learn new coding languages. It also highlights the need for work-based learning opportunities which will help employees gain the necessary skills. Many companies have mentor-style programmes to help employees understand and upgrade the skill-set needed at the workplace.
The report seeks to provide forward-looking insights for recruiters and hiring companies to build a detailed understanding of their talent needs, counter the skills gap in their workforce strategy and build a talent pipeline and also highlight the talent hotspots in India. The research presented in this report has been collected through a mix of analysis from publicly available data and other information sources.
Open Analytics Jobs in India
Top Trends In Data Science Jobs
1) Job Openings in Data science sector in 2019: There has been an overall growth in the number of jobs in analytics and data science ecosystem with India contributing to 6% of open job openings worldwide. The total number of analytics and data science job positions available are 97,000. Out of these, 97% job openings in India are on a full-time basis while 3% are part-time or contractual
Data science job opportunities in future market:
1 Data warehouse architect:
This is sub feild within data engineering.SQL skills are definitely going to be important for this, also solid command of other technical skills is also required.Data management knowledge that you have acquired from data science is needed to be applied here.Other skills are data migration and data visualisation.
2 Business intelligence analyst:
The business intelligence analyst role is highly analytical and requires a balance of IT ,communication and problem solving skills. They transform data into insights that drive business value.They can analyse the trends which will be helpful for other departments in the organisation.
3 statistician:
statistician apply theories and methods to analyse and interpret quantitative data. They may work for companies involved in market research and public opinion with industries concerned with these areas such as quality control and product development.
4 marketing analyst :
marketing analyst look at sales and marketing data to asses and improve the marketing of the company. In digital ers these analyst have great scope to increase the marketing strategies of the companies.
Data science jobs and responsibilities are diverse in the current market. Few of them are as follows:
1. The Data Scientist:
A data scientist is as rare as a unicorn and gets to work everyday with the mindset of a curious data wizard. He/she masters a whole range of skills and talents going from being able to handle the raw data, analyzing that data with the help of statistical techniques, to sharing his/her insights with his peers in a compelling way. No wonder these profiles are highly wanted by companies like Google and Microsoft.
2. The Data Analyst:
The data analyst is the detective of the data science team. Languages like R, Python, SQL and C are mandatory to him/her. Data analysts are wanted by companies like HP and IBM (where they can be teamed up with Watson).
3. The Data Architect:
With the rise of big data, the importance of the data architect’s job is rapidly increasing. The person in this job creates the blueprints for data management systems to integrate, centralize, protect and maintain the data sources. The data architect masters technologies like Hive, Pig and Spark, and needs to be on top of every new innovation in the industry.
4. The Statistician:
The statistician represents what the data science field stands for: getting useful insights from data. With his/her strong background in statistical theories and methodologies, and a logical and stats oriented mindset, he/she harvests the data and turns it into information and knowledge. Statisticians can handle all sorts of data.
5. The Business Analyst:
The business analyst is often a bit different from the rest of the team. While often less technically oriented, the business analyst makes up for it with his/her deep knowledge of the different business processes. Companies looking for business analysts are diverse and active in very different industries. Some examples are Uber, Dell and Oracle.
Mary Kudnavar
Data science is a detailed study of the flow of information from the colossal amounts of data present in an organization's repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills.
The following are the data science jobs currently in the market:-
1. DATA ANALYTICS:-
Data analytics is the science of analyzing raw data in order to make conclusions about that information. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
2 DATA SCIENTISTS:-
A person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision making.
3 STATISTICIAN:-
A statistician is a person who works with theoretical statistics. The profession exists in both the private and public sectors.
4 DATA ARCHITECT:-
Data architect creates blueprints for data management systems. After assessing a company's potential data sources, architects design a plan to integrate, centralize, protect and maintain them.
5. DATA ENGINEER:-
They are just like data scientists, but tend to be less visible because they tend to be further from the end product of the analysis.
6. MARKETING ANALYST:-
Marketing analyst is the practice of managing and studying metrics data in order to determine the ROI of marketing efforts as well as the act of identifying opportunities for improvement.
1. Automation
This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
A series of software tools and algorithms will be used to ingest, filter and highlight data that can be further analyzed.
You’d be forgiven for thinking automation would make data scientists obsolete. A lot of negative talk surrounds automation, particularly about uprooting jobs and careers. That’s not the case in data science, however.
Experts and experienced scientists will still be needed to highlight, identify and implement actionable insights. You can expect to see a lot of automation platforms crop up in the industry over the coming year.
2. Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
Data empowerment is about the alignment or collaboration of everyone involved in a system. It means that everyone has access to the same tools and resources and the same data stores.
More importantly, it means putting data in the hands of the right people — those who can make use of it.
3. Ethics and Influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
The point here is not that the ethics or influence of the industry and related systems will change over the coming year, but that discussions will continue. New applications for data science are discovered on an almost daily basis. Data engineers, scientists, analysts and even administrators will need to join the discussion to let others know now this technology can help others.
After all, you are the ones responsible for creating, developing and maintaining these technologies and systems. What is it you have to share with the world? What can you explain or provide insights about?
You could introduce the positive side of data analytics and collection, for instance, by explaining how it’s helping to fight modern battles and keeping America safer. Or how it’s providing modern conveniences to many — conveniences like personalized shopping campaigns, better home security and more.
2018 will be the year that data science professionals become a part of the greater discourse.
4. Data Lakes and Mass Cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
Cleaning a swamp and converting it into a data lake calls for categorizing, attaching relevant metadata and sorting everything into the appropriate storage segments.
Expect a boom in data restructuring as more organizations and parties realize how beneficial data lakes are.
5. Blockchain App Development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
This attention will call for more demand in the development world as teams look to work with blockchain and implement it into their products, services and systems. In the financial and healthcare industries, for example, a lot of work is being done to introduce blockchain-powered platforms.
The Th Data Science Jobs Available in the Current Market are as follows:-
1) Data Analyst
2) Data Scientist
3) Data Engineer
1) Data Analyst - Data Analyst is considered an 'entry-level' position in the data science field. A data analyst’s primary job is to look at the company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2) Data Scientist - Data Scientists do many same things as Data analysts, but they also build machine learning models to make accurate predictions about the future based on the past data.
A data scientist has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data.
3) Data Engineer - A Data Engineer manages a company’s data infrastructure. Their job requires less statistical analysis and a lot software development and programming skills.
Data Engineer might be responsible for building data pipelines to get the latest sales, marketing and revenue data to data analysts and data scientists quickly and in a usable format
Data science is a multidisciplinary blend of data inference, algorithmm development and technology in order to solve analytically complex problems. Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels. Data science is ultimately about using this data in creative ways to generate business value like
Data Warehouse,Discovery of Data Insight etc
1-Data science – discovery of data insight
This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:
Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
Target identifies what are major customer segments within it's base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences.
Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally.
2-Data science – development of data product
A "data product" is a technical asset that: (*) utilizes data as input, and (*) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data. Here are some examples of data products:
Amazon's recommendation engines suggest items for you to buy, determined by their algorithms. Netflix recommends movies to you. Spotify recommends music to you.
Gmail's spam filter is data product – an algorithm behind the scenes processes incoming mail and determines if a message is junk or not.
Computer vision used for self-driving cars is also data product – machine learning algorithms are able to recognize traffic lights, other cars on the road, pedestrians, etc.
This is different from the "data insights" section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision. In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications. Respective examples of applications that incorporate data product behind the scenes: Amazon's homepage, Gmail's inbox, and autonomous driving software.
Types of Data Science Jobs
1. The Data Analyst:
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2.The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
3.The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product.
Dixita.s.savant
1. Data analyst : This is typically considered an “entry-level” position in the data science field, although not all data analysts are junior and salaries can range widely.A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.As a data scientist, you might be asked to assess how a change in marketing strategy could affect your company’s bottom line. This would entail a lot of data analysis work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make reliable future predictions based on past data.
3. Data engineer
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
MIS Reporting Executive
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems.
Business Analyst
Business analysts are experts in the domain they work in. They try to narrow the gap between business and IT.
Data Analyst
Data analysts they play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
Statistician
Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions.
Data Scientist
Data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance.
Data Engineer
Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Machine Learning Engineer
Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into.
Big Data Engineer
Big data is a big domain, every kind of role has its own specific responsibilities.
1.Data Analyst
A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data Scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data.
3.Data Engineer
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill.
Prachprachishivangekar 2GZ19MBA14
1) Data Scientist is most likely one of the most sizzling job titles that you can have these days
2) The Data Analyst
Languages like R, Python and SQL are part of the data analyst’s basic knowledge. Much like the data scientist role, a broad skillset is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity
3)The Database Administrator
As a database administrator, you ensure that the database is accessible to every stakeholder in the organizations, is performing legitimately and that the necessary safety measures are in place to keep the stored data save
4)Business analyst
A business analyst therefore often performs the role of the middle person between the business folks and the techies.
5)Qualitative expert
they ensure that the decision-maker has fully grasped the shots available for calling. They’re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker
Data Science Job Market Is Booming
Analytics professionals command higher compensation and outearn their peers considerably. As per data from our Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study also found a 1.8% increase in salaries of entry-level analytics professionals with experience between 0 to 3 years. It is also clear that Big Data and Data Engineering professionals who work primarily on unstructured data continue to earn more than analytics professionals.
Furthermore, this year, the entry level professionals ((0-3 years experience) saw a slight increase in salaries, with compensation going up from ₹5.2L median last year to ₹5.3L per annum. At the entry level, almost 76% of analytics professionals earn under 6 lakh per annum.
1.business intelligent analyst:
It includes the technology, applications and practices for collection,integration, analysis and presentation of business information.
2.data mining engineer:
These jobs develop set processes for data mining,and data modeling, and data production.
3. Data architect:
A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying, and managing organizational data architecture.
4.data scientists
They are the big data wranglers,gathering and analyzing large sets of structures and unstructured data.
A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.
6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists.
A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.
6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists. A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.
6. Data Engineer: Data Engineers are the data professionals who prepare the big data infrastructure to be analyzed by Data Scientists.
1. Automation
This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
A series of software tools and algorithms will be used to ingest, filter and highlight data that can be further analyzed.
You’d be forgiven for thinking automation would make data scientists obsolete. A lot of negative talk surrounds automation, particularly about uprooting jobs and careers. That’s not the case in data science, however.
Experts and experienced scientists will still be needed to highlight, identify and implement actionable insights. You can expect to see a lot of automation platforms crop up in the industry over the coming year.
2. Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
Data empowerment is about the alignment or collaboration of everyone involved in a system. It means that everyone has access to the same tools and resources and the same data stores.
More importantly, it means putting data in the hands of the right people — those who can make use of it.
3. Ethics and Influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
The point here is not that the ethics or influence of the industry and related systems will change over the coming year, but that discussions will continue. New applications for data science are discovered on an almost daily basis. Data engineers, scientists, analysts and even administrators will need to join the discussion to let others know now this technology can help others.
After all, you are the ones responsible for creating, developing and maintaining these technologies and systems. What is it you have to share with the world? What can you explain or provide insights about?
You could introduce the positive side of data analytics and collection, for instance, by explaining how it’s helping to fight modern battles and keeping America safer. Or how it’s providing modern conveniences to many — conveniences like personalized shopping campaigns, better home security and more.
2018 will be the year that data science professionals become a part of the greater discourse.
4. Data Lakes and Mass Cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
Cleaning a swamp and converting it into a data lake calls for categorizing, attaching relevant metadata and sorting everything into the appropriate storage segments.
Expect a boom in data restructuring as more organizations and parties realize how beneficial data lakes are.
5. Blockchain App Development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
This attention will call for more demand in the development world as teams look to work with blockchain and implement it into their products, services and systems. In the financial and healthcare industries, for example, a lot of work is being done to introduce blockchain-powered platforms.
Mansi Patil ( 2GI19MBA48)
1.Data Analyst
A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data Scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data.
3.Data Engineer
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill.
Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
4 Types of Data Science Jobs
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
Priyanka Mahindrakar
USN: 2GZ19MBA20
1. MIS Reporting Executive
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
2. Business Analyst
Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
3. Data Analyst
Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
4. Statistician
Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
5. Data Scientist
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook. Almost every company has some sort of a data role on its careers page. Job Descriptions for data scientists and data analysts show a significant overlap.
6. Data Engineer/Data Architect
“Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
7. Machine Learning Engineer
Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
8. Big Data Engineer
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Data analyst - he is a person who looks at the industry data and uses it to ans the business questions and communicates them to other team members
Data scientist - they do similar things like the data analysits but also build machine learning models to make accurate predictions using the past data for the future
Data engineer - he manages a company's data infrastructure. They require more of a software development and programming skill. They are responsible for building data pipeline to get the latest aalessales, marketing data and in a usable format
The data science industry’s job market is hot today, which means that one way to better understand the meaning behind different data science roles is by having a detailed look at all job offerings, their description and their skill requirements.
1)The Data Engineer
The data engineer often has a background in software engineering and loves to play around with databases and large –scale processing systems.
2)The Data Analyst
The data analyst is the Sherlock Holmes of the data science team. Languages like R, Python, SQL and C are elementary to him/her. Just like the data scientist, the skills and talents that are needed for this role are diverse and range the entire spectrum of the data science process combined with a healthy “figure-it-out” attitude. Data analysts are wanted by companies like HP and IBM .
3)The Data Engineer
The data engineer often has a background in software engineering and loves to play around with databases and large –scale processing systems. Thanks to these interests, he/she can easily master technologies and is therefore familiar with a diverse set of languages that span both statistical programming languages and languages oriented more towards web development.
4)The Statistician
Ah the statistician! The historical leader of data and its insights. Although often forgotten or replaced by fancier sounding job titles, the statistician represents what the data science field stands for: getting useful insights from data. With his/her strong background in statistical theories and methodologies, and a logical and stats oriented mindset, he/she harvests the data and turns it into information and knowledge. Statisticians can handle all sorts of data.
5)The Database Administrator
People often say that data is the new gold. This means you need someone who exploits that valuable mine. Enter the Database Administrator. Your DA makes sure that the database is available to all relevant users, is performing properly and is being kept save.
6)The Business Analyst
The business analyst is often a bit different from the rest of the team. While often less technically oriented, the business analyst makes up for it with his/her deep knowledge of the different business processes.
7)Data and Analytics Manager
The cheerleader of the team. A data analytics manager steers the direction of the data science team and makes sure the right priorities are set.
Data science jobs in innovative industries like information technology can take twice as long to fill than the national benchmark average for B.A.+ jobs of 45 days.1 Requirements for data science and analytics jobs are often multidisciplinary and they all require an ability to link analytics to creating value for the organization.
The analytics and technology skills vary widely, but candidates must also demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork. This holistic skill set is rare, so you should expect to compete fiercely for T-shaped individuals, as they are now often called, meaning those with a principle competency, plus well-honed broad skills to help them cross functions or domains.
Common analytics-enabled jobs are Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager. The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency.
These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core.
Strategies for building your own data science and analytics talent pipeline
Here are three strategies to consider now:
Signal for competencies, not just skills, to build your data science team. A competency framework for your company's data science and analytics jobs is a clear signal to policymakers and education providers about what you need.
Identify your high-potential employees for data analytics work and invest in them. Work-based learning opportunities, where teams can work on real business problems, help people gain much-needed experience. Apprentice-mentor style arrangements or specialized development paths can help you pay attention to existing employees with potential for a future in data science.
Connect with other catalysts in your immediate communities. Employers can flex some muscle and influence policymakers and education providers to do more. Act as a community and join a collaborative or other organization focused on creating a competitive workforce for the 21st century.
Data science jobs available in the current market are:
1)Automated Machine Learning (AutoML)
AutomML is growing in prominence. It is an automation layer on top of machine learning (ML), boosting the development efficiency and overall productivity of data scientists. The data science tasks like data preparation, choosing data sources, and feature selection are repetitive and monotonous. AutoML looks into automating these tasks. AutoML can also perform quality modelling and fine-tuning of artificial intelligence (AI) and machine learning algorithms. Giants like Facebook and Google are already using AutoML for their internal data science processes.
2) Quantum Computing
Quantum computers are known to perform complex calculations in supersonic speed, completing the computational tasks in a few seconds. The quantum bits (QuBits) can store a large volume of data and run complex computations in split seconds. Companies like Intel, Google, and IBM, are going to be the pioneers of this industry, but they are yet in their humble beginnings.
3) Edge Computing With Data Science
Edge computing is the technology by which the more amount of computation happens on end devices. Edge computing has pushed IoT one step ahead, and with the data science industry backing the end device computations, edge computing is all set to hit the markets in a big way.
4)Blockchain
Blockchain is a highly secured ledger-based system that requires data science. Notably, data science has shaken hands with blockchain technology to implement new security measures and processes.
VINAY KAMAT(2GZ19MBA49)
1)Data Scientist: He has to Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.
2) Data Analyst: He has to transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.
3) Data Engineer : he has to perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists.
4) Machine Learning Engineer: he has to create data funnels and deliver software solutions.
5) Statistician: he has to interpret, analyze, and report statistical information, such as formulas and data for business purposes.
6) Machine Learning Scientist: he has to research new data approaches and algorithms.
7) Infrastructure Architect: Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.
8) Enterprise Architect: an enterprise architect, Works closely with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes and IT assets.
9) Business Intelligence developer: Business Intelligence developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems
1. IT Systems Analyst
Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests.
2. Healthcare Data Analyst
Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.
The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs.
3. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants.
Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
4. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
5. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
6. Quantitative Analyst
A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc.
7. Data Analytics Consultant
Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.
They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.
8. Digital Marketing Manager
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset.
Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
9. Project Manager
Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.
Project managers need at least a working understanding of data analytics, and often more.
These positions are found internally at large corporations, and frequently in management consulting.
10. Transportation Logistics Specialist
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon or an supply chain organization.
Data science jobs available in the current market :
1) Data Scientist as Statistician :
Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2) Data Scientist as Mathematician
Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientist as Machine Learning Scientists
Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).
4) Data Scientist as Business Analytic Practitioners
Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts.
They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc.
5) Data Scientist as Software Programming Analysts
Unlike traditional coders, this class of professionals have a knack for number crunching through programming. Needless to mention, they are adept at logical thinking and as a result, they take to new programming languages as ducks takes to water. A number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop and the like support data analytics and visualizations.
Software programming analysts have the programming skills to automate routine big data related tasks to reduce computing time. They are also required to handle database and associated ETL (Extract Transform Learn) tools that can extract data, transform it by applying business logic and to load it into visual summary representations such as charts, histograms and interactive dashboards.
6) Data Scientist as Quality Analyst
Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. The job has evolved over the years with new analytic tools which are used by data scientists to prepare interactive visualizations that serve as key inputs in decision making across teams such as management, business, marketing, sales and customer service.
Hayawadan Ayachit 2GI19MBA33
Data science jobs available in the current market :
1) Data Scientist as Statistician :
Statistics knowledge, when clubbed with domain knowledge (such as marketing, risk, actuarial science) is the ideal combination to land a statistician’s work profile. They can develop statistical models from big data analysis, carry out experimental design and apply theories of sampling, clustering and predictive modelling to available data to determine future corporate actions.
2) Data Scientist as Mathematician
Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientist as Machine Learning Scientists
Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting (which can be extrapolated for better inventory management, strengthening supply chain networks, etc.).
4) Data Scientist as Business Analytic Practitioners
Businesses make the final use of all the number crunching done by data science professionals. As a business analytic professional it is important to have business acumen as well as know your numbers. Business analysis is a science as well as art and one cannot afford to be driven entirely by either business acumen or by insights obtained based on data analysis. These professionals sit between front end decision making teams and the back end analysts.
They work on crucial decision making such as ROI analysis, ROI optimization, dashboards design, performance metrics determination, high level database design, etc.
5) Data Scientist as Software Programming Analysts
Unlike traditional coders, this class of professionals have a knack for number crunching through programming. Needless to mention, they are adept at logical thinking and as a result, they take to new programming languages as ducks takes to water. A number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop and the like support data analytics and visualizations.
Software programming analysts have the programming skills to automate routine big data related tasks to reduce computing time. They are also required to handle database and associated ETL (Extract Transform Learn) tools that can extract data, transform it by applying business logic and to load it into visual summary representations such as charts, histograms and interactive dashboards.
6) Data Scientist as Quality Analyst
Quality Analyst has for long been associated with statistical process control in manufacturing industry. This position has been included here to emphasize the importance of data science in core industries. Assembly lines involved in mass production have large data sets to be analysed to maintain quality control and meet minimum performance standards. The job has evolved over the years with new analytic tools which are used by data scientists to prepare interactive visualizations that serve as key inputs in decision making across teams such as management, business, marketing, sales and customer service.
Ninad Patil (2GZ19MBA03)
1. Automation : This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
2. Data lakes and mass cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
3. Block chain app development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
4. Ethics and influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
5. Data empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
SHREYA KAPASE
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Data science combines several disciplines, including statistics, data analysis, machine learning, and computer science. This can be daunting if you’re new to data science, but keep in mind that different roles and companies will emphasize some skills over others, so you don’t have to be an expert at everything. There are potential data science jobs for lots of different experience levels.
Different types:
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis. You’ll see job postings listed under both “Data Scientist” and “Data Engineer” for this type of position. Since you’d be (one of) the first data hires, heavy statistics and machine learning expertise is less important than strong software engineering skills.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
UnknownMarch 28, 2020 at 8:45 PM
1. Data analyst : This is typically considered an “entry-level” position in the data science field, although not all data analysts are junior and salaries can range widely.A data analyst’s primary job is to look at company or industry data and use it to answer business questions, then communicate those answers to other teams in the company to be acted upon.
2.Data scientist
Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.As a data scientist, you might be asked to assess how a change in marketing strategy could affect your company’s bottom line. This would entail a lot of data analysis work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make reliable future predictions based on past data.
3. Data engineer
A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill. At a company with a data team, the data engineer might be responsible for building data pipelines to get the latest sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access past data.
(1) Data Scientist as Statistician
This is data analysis in the traditional sense. The field of statistics has always been about number crunching. A strong statistical base qualifies you to extrapolate your interest in a number of data scientist fields. Hypothesis testing, confidence intervals, Analysis of Variance (ANOVA), data visualization and quantitative research are some of the core skills possessed by statisticians which can be extrapolated to gain expertise in specific data scientist fields
2) Data Scientist as Mathematician
Mathematicians have conventionally been related with extensive theoretical research but emergence of big data and data science have changed that perception. Mathematicians have been gaining more acceptance into the corporate world than ever before, owing to their deep knowledge of operations research and applied mathematics. Their services are sought after by businesses to carry out analytics and optimization in various fields such as inventory management, forecasting, pricing algorithm, supply chain, quality control mechanism and defect control. Defence and military organizations also seek mathematicians to carry out crucial big data assignments such as digital signal processing, series analysis and transformative algorithms.
3) Data Scientists Vs Data Engineers
These are often confused with data scientists. However, a data engineer’s role is very different from that of a data scientist. A data engineer has the responsibility to design, build and manage the information captured by an organization. He is entrusted with the job of putting in place a data handling infrastructure to analyse and process data in line with an organization’s requirements. Additionally, he is also responsible for its smooth functioning. They need to work closely with data scientists, IT managers and other business leaders to translate raw data into actionable insights which would result in competitive edge for the organization.
4) Data Scientist as Machine Learning Scientists
Computer systems around the world are increasingly being equipped with artificial intelligence and decision making capabilities. They possess neural networks that are programmed for adaptive learning – meaning they can be trained over a period of time to make same decisions when same set of inputs is given to them. Machine Learning Scientists develop such algorithms which are used to suggest products, pricing strategies, extract patterns from big data inputs and most importantly, demand forecasting.
ABHISHEK PATIL
Data science job is used to analyse data for actionable insights and it describes jobs that are drastically different from one another in current market structure .
1. The Data Analyst
There are some companies where being a data scientist is synonymous with being a data analyst. Your job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account.
2. The Data Engineer
Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
3. The Machine Learning Engineer
There are a number of companies for whom their data (or their data analysis platform) is their product. In this case, the data analysis or machine learning going on can be pretty intense. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path.
4. The Data Science Generalist
A lot of companies are looking for a generalist to join an established team of other data scientists. The company you’re interviewing for cares about data but probably isn’t a data company. It’s equally important that you can perform analysis, touch production code, visualize data, etc.
Big Data Analytics Companies are used to analyze large-scale datasets that are too big to process using traditional methods. This Big Data Analytics Applications tutorial introduces the concept of Big Data Analytics Applications, including the goals, challenges, and techniques used to analyze them.
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