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. Data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. In today’s high-tech world, everyone has pressing questions that must be answered by “big data”. From businesses to non-profit organizations to government institutions, there is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes.

Data science is arguably the hottest career of the 21st century and continues to evolve as one of the most promising and in-demand career paths for skilled professionals.

Examples of data science usage in business world

Tesla, Ford and Volkswagen are all implementing predictive analytics in their new wave of autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.

Do you ever wonder how Netflix knows just what movies you’ll love to binge? Using data science Netflix collect data from subscribers and implementing data analytics models to discover customer behaviour and buying patterns. Then, using that information to recommend movies and TV shows based on their subscribers’ preferences.

International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis.


In India data science courses are mainly available for engineering graduates! A data science aspirant can earn a bachelor’s degree in IT, Computer Science, Mathematics, Physics, Statistics or related subjects and pursue specialization in data science at PG level (M. Tech / PGDM / MBA).    

There are few institutes offering B Tech. programs in Data Science.

Required Skills

Each role in data science requires specific skills and knowledge. However, in general the skills required for data science include:

  • Data Visualization and Communication
  • Data Wrangling
  • Hadoop
  • Machine learning
  • Multivariable Calculus and Linear Algebra
  • Programming Languages (Java, Scala)
  • Programming Skills (SAS, R, Python)
  • SQL
  • Statistical and Mathematical skills

Eligibility Criteria

The basic eligibility requirement for admissions to any B. Tech. / BSc / BCA programme is:

  • Candidates must have passed Class 10+2 exam from a recognised board with Physics, Chemistry and Mathematics as core subjects.
  • They must also have secured a minimum aggregate mark of 60% in the above subjects combined.

Most of the institutions requires a bachelor’s degree in relevant field of engineering or equivalent with at least 55% marks. The eligibility criteria may vary from institute to institute.

Please see below eligibility criteria of some institutions.

IIT: GATE score or with a B-Tech degree/MSc (Math/Applied Math) degree from any IIT.

IISc: B. E. / B. Tech./ MSc / MCA / Four year B.S. or equivalent in any science/engineering discipline, and a valid GATE Score in any paper, are eligible. A strong background in Mathematics and Programming is required.

VIT: B.E / B. Tech Degree in any Branch / MCA or any other equivalent degree. Candidates should have graduated with a full-time degree from any recognized University/Institute with a minimum aggregate of 60% or First class

Amrita School of Engineering: B.E. / B. Tech. (CSE, IT, ECE, EEE, EIE, Information Science), MCA, MSc Computer Science, MSc IT, MSc Software Engineering.

Jain University: B.E / B. Tech in EC, EEE or CS / MSc in Electronics

The eligibility criteria may vary from institute to institute.

Entrance Examination

JEE Main is the main entrance examination for BE/BTech admission in important institutes including National Institute of Technology (NIT), Indian Institute of Information Technology (IIIT), Government Funded Technical Institutes (GFTI), etc.

JEE Advanced is the entrance for BE/BTech admission in Indian Institute of Technology (IIT).

GATE Graduate Aptitude Test in Engineering is the entrance for ME/MTech postgraduate programs

State Level Engineering College admission is based on state level entrance exams, for example:

  • APEAMCET Andhra Pradesh
  • COMEDK Consortium of Medical, Engineering and Dental Colleges of Karnataka.
  • KCET Karnataka Common Entrance Test
  • KEAM Kerala Engineering Agriculture and Medicine
  • PGCET Karnataka Post Graduate Common Entrance Test
  • TNPCEE Tamil Nadu Professional Courses Entrance Examination

For most state level entrance examinations, 50% weightage is for Board Exams. 

Private university / college entrance examinations

  • BITSAT Birla Institute of Technology & Science
  • CUET Christ University Entrance Test
  • JET Jain Entrance Test
  • KITEE KIIT University
  • MET Manipal Entrance Test
  • SRMJEEE SRM University
  • VITEE Vellore Institute of Technology

Job Profiles

Data Science is considered as a hot new field that promises to revolutionize industries from business to government, health care to academia. However, there are a variety of different jobs and roles under the data science umbrella to choose from.

Here is a comprehensive list:

  • Business Analyst
  • Data Analyst
  • Data and Analytics ManagerData Architect
  • Data Engineers
  • Data Scientist
  • Database Administrator
  • Machine Learning Engineer
  • Statistician

Top Recruiters

There are demands for engineering graduates in several domains including the private and public sector. Several companies recruit engineers in various capacities. Here are some examples of top companies recruiting BE/BTech graduates

  • Accenture
  • Adobe
  • Amazone
  • Apple
  • Bloomberg
  • Facebook
  • Google
  • IBM
  • Intel
  • Microsoft
  • Oracle
  • PayPal
  • Procter & Gamble
  • Spotify
  • Twitter
  • Uber


Indian Institute of Technology (IITs) are the most prestigious institutes.  There are 23 IITs across India, with a total intake of over 11,000 for BTech undergraduate programs in various branches. IITs also offer postgraduate program (MTech) and Bachelor-Master dual degree (BTech + MTech in 5 years) and Doctoral degree (PhD).

Indian Institutes of Information Technology (IIITs) are autonomous institutions offering technical education focused on the Information Technology and Communication Studies. IIITs have gained popularity among students are considered among the top colleges only after  Indian Institutes of Technology (IITs) and National Institutes of Technology (NITs). However, there are a total of 25 IIITs in India, five of which are listed as the Institutes of National Importance while the remaining 18 IIITs are set up on the Public-Private Partnership (PPP) model

National Institutes of Technology (NITs) is there one in most states. There are 31 NITs with a total intake of over 10,000 for BTech undergraduate programs. Most NITs offer MTech as well as Bachelor-Master dual degrees.

Government Funded Technical Institutes (GFTI). There are 23 GFTIs with a total intake of over 9,000 seats for BTech.

In every state in India, there are Government Engineering Colleges, Aided Engineering Colleges and Self-Financing Engineering Colleges. State Level Engineering Colleges are available in this link.

Before joining a course, please ensure that the course in the given institution is approved by All India Council for Technical Education (AICTE).

Questions & Answers​

Q) What is the difference between Data Scientist and Data Engineer?

A) There is a significant overlap between data engineers and data scientists when it comes to skills and responsibilities. The main difference is the one of focus.  Data Engineers are focused on building infrastructure and architecture for data generation.  In contrast, data scientists are focused on advanced mathematics and statistical analysis on that generated data.  

Data Scientists are engaged in a constant interaction with the data infrastructure that is built and maintained by the data engineers, but they are not responsible for building and maintaining that infrastructure. Instead, they are internal clients, tasked with conducting high-level market and business operation research to identify trends and relations—things that require them to use a variety of sophisticated machines and methods to interact with and act upon data.

In contrast, data engineers work to support data scientists and analysts, providing infrastructure and tools that can be used to deliver end-to-end solutions to business problems.  Data engineers build scalable, high performance infrastructure for delivering clear business insights from raw data sources; implement complex analytical projects with a focus on collecting, managing, analyzing, and visualizing data; and develop batch & real-time analytical solutions.

Q) How Data Science differs from Big Data and Data Analytics?

A) Data Science is a field which contains various tools and algorithms for gaining useful insights from raw data. It involves various methods for data modelling and other data related tasks such as data cleansing, preprocessing, analysis, etc. Big Data implies the enormous amount of data which can be structured, unstructured and semi-structured generated through various channels and organizations. The tasks of Data Analytics involve providing operational insights into complex business situations. This also predicts the upcoming opportunities which the organization can exploit.

Q) How do Data Scientists use statistics?

A) Statistics plays a powerful role in Data Science. It is one of the most important disciplines to provide tools and methods to find structure in and to give deeper insight into data. It serves a great impact on data acquisition, exploration, analysis, validation, etc.

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