Admission Enquiry

Prepare for a career in Data Science with the most comprehensive master’s degree programme in Data Science & Engineering without taking a break from your career.

M.Tech. Data Science and Engineering is a four-semester programme designed for working professionals that helps learners build mathematical and engineering skills required to advance their career as a Data Scientist or Data Engineer .

Admission are currently open. Programme begins on Nov 7, 2020. For detailed programme information, download the brochure below.

Option to pay fees using easy EMI with 0% interest.

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Admission Enquiry

Please fill the below fields for fee details, programme information, and application instructions

A bright future for Data Science professionals

Profile of M.Tech. Data Science and Engineering participants

Professional Experience of Participants

Major Organisations where Participants work

Organisations where participants are employed at the time of joining the programme

Students Speak

  • Programme Highlights
  • Programme Curriculum
  • Learning Methodology
  • Eligibility
  • Fee Structure
  • How to Apply
  • Online Exams Option

Programme Highlights

  • Learn without a career break with live online lectures conducted mostly on weekends or after office hours by BITS Pilani faculty members and experienced industry professionals
  • The curriculum covers areas that prepare you for most lucrative careers in the space of Data Science, Data Engineering and Advanced Analytics. It helps learners master critical skills such as Mathematical modeling, Machine learning, Artificial Intelligence, Product development and scripting languages.
  • Benefit from Case Studies, Simulations, Virtual Labs & Remote Labs that allow learners to apply concepts to simulated and real-world situations. Tools & Technologies covered include Apache Spark, Apache Storm for Big Data Systems/ Real-time Processing, Tableau for data visualization, Tensorflow for Deep Learning and various packages within Python for data processing, machine learning and data visualization.
  • Live weekly online lectures, supplementary online contact sessions comprising of tutorials, doubt-clearing sessions, and industry talks will also be conducted periodically.
  • Contact-less and safe Online exams facility.
  • The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learnt during the programme.
  • The programme uses a Continuous Evaluation System that assesses the learners over convenient and regular intervals. Such a system provides timely and frequent feedback and helps busy working professionals stay on course with the programme.
  • Learning Continuity Assurance facility, that allows participants to take a break without losing the semester fee paid by them.  
  • Fee payment by easy-EMIs with 0% interest.

Programme Curriculum

Semester-wise Pattern

Participants need to take at least 12 courses towards coursework and complete one Project/ Dissertation. The coursework requirement for the programme would consist of a set of core courses and electives. Core courses are compulsory for all participants, while electives can be chosen based on individual learning preferences.

First Semester

  • Data Mining
  • Mathematical Foundations for Data Science
  • Data Structures and Algorithms Design
  • Computer Organization and Systems Software

Second Semester

  • Introduction to Statistical Methods
  • Introduction to Data Science
  • Machine Learning
  • Elective 1

Third Semester

  • Elective 2
  • Elective 3
  • Elective 4
  • Elective 5

Fourth Semester

  • Dissertation


  • Deep Learning
  • Natural Language Processing
  • Artificial-Intelligence
  • Real time-Analytics
  • Data Visualisation
  • Graphs - Algorithms & Mining
  • Optimization Methods for Analytics
  • Big Data Systems
  • Advanced Topics in Data Processing
  • Information Retrieval
  • Probabilistic Graphical Models
  • Data Warehousing
  • Systems for Data Analytics
  • Ethics for Data Science

Course Wise Syllabus

1. Introduction to Data Science

  • Learn about the need for data science, with emphasis on data; Visualization and ethical aspects involved in data science and engineering processes; Various applications of data science

2. Mathematical Foundations for Data Science

  • Learn about concepts in linear algebra and use it as a platform to model physical problems; Analytical and numerical solutions of linear equations; Mathematical structures, concepts, and notations used in discrete mathematics

3. Introduction to Statistical Methods

  • Learn about basic and some advanced concepts of probability and statistics; Concepts of statistics in solving problems arising in data science

4. Data Structures and Algorithms Design

  • Learn about applications of basic and advanced data structures & algorithms; How to determine the space and time complexities of various algorithms; Identifying and choosing the relevant data structures and algorithms for a given problem and justifying the time and space complexities involved

5. Computer Organization & Software Systems

  • Learn about computer organization, architecture aspects, and operating system concepts; Advanced systems and techniques used for data processing

6. Systems for Data Analytics

  • Learn about fundamentals of data engineering; Basics of systems and techniques for data processing - comprising of a relevant database, cloud computing, and distributed computing concepts

7. Data Mining

  • Learn about data pre-processing & cleaning; Association rule mining, classification, clustering techniques

8. Machine Learning

  • Learn about basic concepts and techniques of Machine Learning; Using recent machine learning software for solving practical problems; How to do independent study and research in the field of Machine Learning

9. Data Visualization

  • Learn about design principles, human perception and effective storytelling with data; Modern visualization tools and techniques

10. Ethics for Data Science

  • Learn about the need for data ethics; Challenges of data privacy; Data policies for maintaining the privacy of data; Data Privacy

11. Graphs - Algorithms and Mining

  • Learn about concepts of graph theory to understand; How Graph theory concepts are used in different contexts, ranging from puzzles and games to social sciences/ engineering/ computer science; Model problems in real-world using graphs; Applying mining algorithms to get information from graph structures

12. Optimization Methods for Analytics

  • Learn about applying linear programming techniques to complex business problems across various functional areas including finance, economics, operations, marketing, and decision making; Implementing optimization techniques to business and industrial problems

13. Big Data Systems

  • Learn about concepts related to big data and its processing; Applying the concepts of storage, retrieval, interfaces, and processing frameworks to a given problem and design solutions for the same by choosing the relevant ones

14. Advanced Topics in Data Processing

  • Learn about advanced strategies for data processing; The relationship between the scale of data and the systems used to process it; The importance of scalability of algorithms as the size of datasets increase

15. Information Retrieval

  • Learn about structure and organization of various components of an IR system; Information representation models, term scoring mechanisms, etc. in the complete search system; Architecture of search engines, crawlers, and the web search; Cross-lingual retrieval and multimedia information retrieval

16. Deep Learning

  • Learn about deep learning techniques, constructing deep network structures specific to applications and tuning for parameters

17. Natural Language Processing

  • Learn about natural language processing techniques such as Parts-of-Speech tagging, syntactic and semantic modelling of languages

18. Artificial Intelligence

  • Learn about classic AI Techniques

19. Real-time analytics

  • Learn about Processing frameworks for real-time analytics, and Analytics techniques for real-time streaming data

20. Probabilistic Graphical Models

  • Learn about representation, learning and reasoning techniques for graphical models

21. Data Warehousing

  • Learn about concepts needed to design, develop, and maintain a data warehouse; End-user access tools like OLAP and reporting

For fee details and programme information, please click here to download the Programme Brochure


Learning Methodology


The programme emphasises on Experiential Learning that allows learners to apply concepts learnt in classroom in simulated and real work situations. This is achieved through:

  • Tools & Technologies: Apache Spark, Apache Storm for Big Data Systems/ Real time Processing; Tableau for data visualisation; Tensorflow for Deep Learning; Various Packages within Python for data processing, machine learning, data visualization etc.
  • Case Studies and Assignments: Carefully chosen real-world cases & assignments are both discussed and used as problem-solving exercises during the programme
  • Dissertation/ Project Work: The fourth semester offers an opportunity for learners to apply their knowledge gained during the programme to a real-world like complex project. The learner is expected to demonstrate understanding of vital principles learnt across semesters and their ability to successfully apply these concepts.


Lectures are conducted live via online classes. These lectures can be attended via the internet using a computer from any location. These online classrooms offer similar levels of interactivity as regular classrooms at the BITS Pilani campus.

Classes for students admitted during the period Aug-Sep 2020 will begin in Oct 2020.

The online lectures are conducted usually over weekends for a total of 7-8 hours per week. If you miss a lecture, you can also access the recorded lecture on the internet.

Lectures are conducted on Sat/Sun as per Indian Standard Time.


In addition to live weekly online lectures, supplementary live online sessions will be organised periodically comprising of tutorials, doubt-clearing interactions, and industry talks (18-20 hours per semester).


The learners' performance is assessed continuously throughout the semester using various tools such as quiz, assignments, mid-semester and comprehensive exams. The assessment results are shared with the learners to improve their performance. Each course will entail a minimum of 1 Assignment/ Quiz, a Mid-semester exam and a final Comprehensive exam. Your semester calendar will clearly indicate the dates of the Mid-semester and Comprehensive exam.

Typically, a Mid-semester or Comprehensive examination for a course is for 2-3 hours duration. The examinations are typically conducted over a weekend, i.e. Saturday and Sunday.


Minimum eligibility to apply - Employed professionals holding B.E. / B.Tech. / MCA / M.Sc. or equivalent with at least 60% aggregate marks or more in their qualifying exam, and minimum one-year relevant work experience are eligible to apply.

Applicants should possess basic programming knowledge and adequate background in Mathematics.

Fee Structure

For fee details, programme information and application instructions, please click here to download the Programme Brochure.

How to Apply

For application instructions, programme information and fee structure, please click here to download the Programme Brochure

Online Exams Option

Online Exams Option

Participants will be able to take examinations online from any location e.g. office or home. To take an online examination, student must possess a laptop or desktop with a web cam, a smart phone and good internet connectivity. These examinations will be either remotely proctored or non-proctored.

Participants may also request to take the exam at an examination center designated by BITS Pilani in locations such as Bangalore, Chennai, Hyderabad, Pune, Mumbai, Goa, Delhi NCR, Pilani, Kolkata, and Dubai.

Admission are currently open. Programme begins on Nov 7, 2020.

download brochure