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BTech Data Science

An advanced BTech degree to take you on a high-growth career path.

Our daily lives generate more data than ever before due to the adoption of digital technologies. With the advent of IoT (Internet of Things) and Industry 4.0, the volume of data is growing at an exponential rate. In that wealth of data lie insights that can be used to change our world for the better. This has led to the matured discipline of Data Science that involves collection, visualisation, processing and modelling of large and complex data sets from different domains and sources.
Data Science is considered the fourth paradigm of science, after Empirical, Theoretical, and Computational paradigms. Since 2014, NIIT University (NU) has been offering data science-related courses, specialisations, and industry-linked programmes. Our students have excelled both in industry and academia and continue to make a mark as able, sought-after data scientists.

NU’s BTech Data Science programme is a winning combination of more than eight years of experience in the field combined with insights from trends across academic institutions and industry.

A 4-year undergraduate programme that prepares students to acquire, manage, and elicit meaning from data for improved decision-making in the business world, the BTech Data Science programme will give students the knowledge, skills and tools needed to handle complex data from all possible domains.

Key differentiators

Like all our other flagship programmes, the BTech Data Science course is designed around NU’s core principles of providing industry-linked, technology-based, research-driven and seamless education.

Our BTech Data Science programme is an advanced course that puts you into a high-growth journey. Here’s how:
Consistent with our core principle of industry-linkage, the hallmark of the NU BTech Data Science programme is its deep rootedness into industry. Industry professionals work as mentors along with NU faculty giving our students the dual advantage of academic rigor and industry relevance. The curriculum has been designed by faculty, data scientists and industry subject matter experts.
Acknowledging the huge demands of Data Science professionals in the industry today, the programme in Data Science has been designed to create trained Computer Science graduates to fulfill the requirements of the industry. The programme content is co-designed with our industry partner, IBM. Students go through a set of professional electives, during sixth and seventh semesters, offered in collaboration with IBM. Subsequently, selected students do their six month industry practice at IBM.
We are very impressed with both the skills and attitude of NU graduates who have gone through the Analytics and Cognitive (Data Science) programme. They demonstrate terrific aptitude and attitude towards learning. We need more such graduates and they are performing significantly above the mass hired engineering graduates we hire from the top engineering institutions. The curriculum for the programme is jointly designed by IBM (Cognitive group) and NU faculty and reflects the dynamic and changing requirements in the market place.” — Vijay Muralidharan, Data Science Leader, Cognitive & Advance Analytics CIC, IBM.
The programme offers an immersive experience. Students of BTech Data Science work on two capstone projects, one research & development project, and engage in a 6-month long Industry Practice.
Faculty in the BTech Data Science area at NU are from well-known universities like IIT-ISM Dhanbad; Missouri University of Science and Technology, Missouri, USA; Ohio University, Ohio, USA; and University of Minnesota, Minneapolis, USA. Our faculty comes with rich prior work experience in teaching, research, industry and the government. Their research has been published in several international journals and conference proceedings. Our faculty members have been preparing industry-ready BTech Data Science specialists for more than six years.
True to the spirit of being a technology-based university, NU offers advanced computing machines, software, cloud services and high-tech laboratories to aid students in their learning journey.
A winning combination of the “flipped classroom” model along with a unique mastery-learning platform is integrated into the project-based learning approach at NU. This approach helps students to develop independent learning skills and builds a deeper understanding of subjects.

Meet our faculty

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Prof Debashis Sengupta

Professor and Area Director

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Prof Ratna Sanyal

Professor, Computer Science & Engineering & Dean - Research

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Prof Sudip Sanyal

Professor and Dean, Faculty of Engineering and Technology

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Dr Deepika Prakash

Assistant Professor

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Dr Shweta R Malwe

Assistant Professor

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Dr Suranita Kanjilal

Assistant Professor

Programme outline

Students must complete a total of 176 credits spread over 39 courses and 2 Capstone Projects, 1 R&D Project and 1 Industry Practice in Data Science and related subject areas to obtain a BTech degree in Data Science.
Course category Credits
Mathematics and Basic Sciences (MBS) 20
Engineering Sciences (ESC) 14
Humanities and Social Sciences (HSS) 18
Professional Core Course (PCC) 28
Data Science Core Course (DS) 32
Professional Electives Course (PEC) 20
Open Electives Course (OEC) 12
Project Work, Internship and Industry Practice (PRJ) 32
Environmental Sciences (EVS) Audit Course
Total credits 176
Professional elective courses

Professional elective courses

Social Media Analytics
Cloud Computing Concepts
Modeling & Simulation
Multimodal data processing & analysis
Numerical Methods for Data Science
Dimensional and NoSQL Databases
Cognitive Computing
Data Stream Mining
Data Integrity and Privacy
Deep Learning
Statistical Machine Learning
Artificial Intelligence
Data Mining
Computer Vision
Business Analytics
Predictive Modelling for Data Science
Big Data Concepts
Artificial Neural Network
Machine Learning
Information Retrieval
Web Intelligence and Algorithms

Programme architecture

Year I (Semester I & Semester II)

A systematic exposure to scientific, mathematical and engineering principles will be given to the students during the first two semesters. In each semester, students will take one course each in Physics, Chemistry, Mathematics, Electronics, Foundation of Computer Programming, Workshop Practice, Engineering Graphics, Data Structures, along with Technical English.
#Course codeCourse LTPC


MAT 112Calculus3104


 Science – I3024


EL 111Fundamentals of Electronics3104


TA 111Fundamentals of Computer Programming (Python)2044


TA 212Workshop Practice1043


TA 102Communication Skills2023


NU 111Community Connect0021
  Total semester L-T-P-C1421222
#Course codeCourseLTPC
1MAT 101Algebra and Differential Equations3104
2 Science – II3024
3ENV 301Environmental Science3003
4CS 102Data Structures (with Python)3024
5TA 202Engineering Graphics2023
6 HSSM-I3003
7NU 111Community Connect0021
  Total semester L-T-P-C171621

Year II (Semester III & Semester IV)

At the beginning of the third semester, each student will enter his/her chosen area (Data Science). Students are required to complete 46 redits in Year II (Semester III & Semester IV).
#Course codeCourseLTPC
1MAT 221Probability & Random Process3104
2 Foundations of Data Science3024
3CS 232Discrete Mathematics3104
4CS 251Object Oriented Programming (with Java)2044
5 Data Visualisation2044
6 HSSM-II3003
7NU 211Community Connect0021
  Total semester L-T-P-C1631023


#Course codeCourseLTPC
1CS 201Design & Analysis of Algorithms3034
2 Statistical Methods for Data Science3024
3 Optimisation for Data Science3024
4CS 211Operating Systems3024
5CS 231Database Management Systems3024
6 HSSM-III3003
7NU 212Community Connect0021
  Total semester L-T-P-C1801123

Year III (Semester V & Semester VI)

In their third year of study, each student will have a choice of selecting one open elective course in Semester V and two ‘Data Science’ related professional elective courses in Semester VI, along with one Capstone Project-I and one R & D Project. Students are required to complete 46 credits in their third year (Semester V & Semester VI).
#Course codeCourse LTPC
1 Foundations of Machine Learning3024
2CS 491Natural Language Processing & Text Analytics3024
3EL 302Digital Image Processing3024
4DS 412Inferential Statistics for Data Science2044
5 Open Elective – I3024
6 HSSM-IV3003
7NU 311Community Connect0021
  Total semester L-T-P-C1701223
#Course codeCourseLTPC
1 Introduction to Artificial Intelligence & Deep Learning3024
2 Professional Elective – I3024
3 Professional Elective – II3024
4CS 392Capstone Project — I1064
5NU 302R & D Project1064
6 HSSM-V3003
7NU 312Community Connect0021
  Total semester L-T-P-C1401823

Year IV (Semester VII &Semester VIII)

In Semester VII, students have a choice of selecting three professional elective courses and two open elective courses, along with Capstone project II. Students are required to complete 44 credits in their Year IV (Semester VII and Semester VIII). In the final semester, the students are required to complete Industry Practice.
#Course codeCourse LTPC
1 Professional Elective – III 3024
2 Professional Elective – IV 3024
3 Professional Elective – V 3024
4 Open Elective – II 3024
5 Open Elective – III 3024
6 Capstone Project — II 1064
7NU 312Community Connect 0021
  Total semester L-T-P-C 1601624
# Course Code Course Title L T P C
1 NU402 Industry Practice / Project 0 0 40 20
Total semester L-T-P-C 0 0 40 20
Programme specific outcomes

Programme specific outcomes


Understand, analyse and develop essential proficiency in the areas related to Data Science and underlying statistical and computational principles, Optimisation techniques and apply the knowledge to solve practical problems
Ability to implement Data science techniques along with Artificial Intelligence inferential statistics, predictive modeling, neural networks, natural language processing, machine learning, data visualisation and big data analytics for solving a problem and designing novel algorithms for successful career and entrepreneurship
Use modern tools, technologies, and programming languages in the area of Data science
Apply the concepts and practical knowledge in analysis, design and development of data driven decision making systems and applications to solve multi-disciplinary problems
Ability to develop solutions for prediction and forecasting to industry and societal needs in a rapid changing technological environment and communicate with clients as an entrepreneur
To provide a concrete foundation and enrich their abilities to qualify for employment, higher studies and research in Data science and Artificial intelligence with ethical values
Pursue higher studies and continue to learn by participating in conferences, seminars and by doing individual and group research in Data science and related areas

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