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BTech Artificial Intelligence and Data Science

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

NIIT University (NU) has been offering Artificial Intelligence and Data Science-related courses, specializations, and industry-linked programmes since 2014. Our students have excelled both in industry and academia and continue to make a mark as able, sought-after data scientists.

Artificial Intelligence and Data Science is considered the fourth paradigm of science, after Empirical, Theoretical, and Computational paradigms. 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 Artificial Intelligence and Data Science that involves collection, visualization, processing and modelling of large and complex data sets from different domains and sources.

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

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

BTech Artificial Intelligence and Data Science – Distinguishing Features

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

Our BTech Artificial Intelligence and 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 B Tech Artificial Intelligence and 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.
quotes 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 program is jointly designed by IBM (Cognitive group) and NU faculty and reflects the dynamic and changing requirements in the market place.” — Vijay Muralidaran, Data Science Leader, Cognitive & Advance Analytics CIC, IBM.

The programme offers an immersive experience. Students of BTech Artificial Intelligence and Data Science work on two capstone projects, one research & development project, and engage in a 6-month long Industry Practice.

Faculty in the BTech Artificial Intelligence and Data Science course 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.

Several unique teaching methods are 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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Dr Achintya Roy

Assistant Professor

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Dr Dinesh Kumar

Assistant Professor

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Dr Jayraj Singh

Assistant Professor

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Prof Ratna Sanyal

Professor and Dean-Research

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Dr Shweta R Malwe

Assistant Professor

BTech Artificial Intelligence and Data Science – Course Outline

Students at NU’s BTech Artificial Intelligence and Data Science programme 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 Artificial Intelligence and 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

List of Professional Elective Courses in Artificial Intelligence and Data Science

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

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

BTech Data Science Course Syllabus & Structure

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 code Course L T P C


MAT 112 Calculus 3 1 0 4


Science – I 3 0 2 4


EL 111 Fundamentals of Electronics 3 0 2 4


TA 111 Fundamentals of Computer Programming 2 0 4 4


TA 202/ TA 212 Engineering Graphics / Workshop Practice 2/1 0 2/4 3


TA 102 Communication Skills 2 0 2 3


NU 111 Community Connect 0 0 2 1
Total Semester L-T-P-C 13 1 12 23
# Course code Course L T P C
1 MAT 101 Algebra and Differential Equations 3 1 0 4
2 Science – II 3 0 2 4
3 CS 102 Data Structures 3 0 2 4
4 EL 101 Digital Logic and Circuit 3 0 2 4
5 TA 212/ TA 202 Workshop Practice/Engineering Graphics 2/1 0 4/2 3
6 HSSM-I 3 0 0 3
7 NU 111 Community Connect 0 0 2 1
8 Total Semester L-T-P-C 15 1 10 24

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 code Course L T P C
1 MAT 221 Probability & Random Process 3 1 0 4
2 CS 122 Computer Architecture and Organization 3 0 2 4
3 CS 201 Design and Analysis of Algorithms 3 0 2 4
4 CS 232 Discrete Mathematics 3 1 0 4
5 CS 251 Object Oriented Programming 2 0 4 4
6 CS 322 Artificial Intelligence 3 0 2 4
7 NU 211 Community Connect 0 0 2 1
Total semester L-T-P-C 17 2 12 25
# Course code Course L T P C
1 CS 231 Database Management Systems 3 0 2 4
2 EL 302 Digital Image Processing 3 0 2 4
3 CS 211 Operating Systems 3 0 2 4
4 DS 412 Inferential Statistics for Data Science 3 0 2 4
5 CS 212 Computer Networks and Data Communication 3 0 2 4
6 CS 4131 Machine Learning 3 0 2 4
7 NU 212 Community Connect 0 0 2 1
Total semester L-T-P-C 18 0 14 25

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 code Course L T P C
1 DS 401 Numerical Methods for Data Science 3 1 0 4
2 CS 3132 Cloud Computing Concepts (from CSE PE – Sem-VI) 3 0 2 4
3 CS 4261 Natural Language Processing & Text Analytics 3 0 2 4
4 CS 4101 Introduction to Linear and Non-linear Optimization (OE) 3 1 0 4
5 DS 432 Predictive Modeling for Data Science 3 0 2 4
6 HSSM-II 3 0 0 3
7 NU 311 Community Connect 0 0 2 1
Total semester L-T-P-C 18 2 8 24
# Course code Course L T P C
1 CS 3102 Dimensional and NoSQL Databases 2 0 4 4
2 Professional Elective – I 3 0 2 4
3 Professional Elective – II 3 0 2 4
4 CS 392 Capstone Project – I 2 0 4 4
5 NU 302 R & D Project 1 0 6 4
6 HSSM-III 3 0 0 3
7 NU 312 Community Connect 0 0 2 1
Total semester L-T-P-C 14 0 20 24

Year IV (Semester VII &Semester VIII)

In Semester VII, students of the BTech Data Science programme 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 code Course L T P C
1 Professional Elective – III 2 0 4 4
2 Professional Elective – IV 3 0 2 4
3 Professional Elective – V 2 0 4 4
4 Open Elective – II 3 0 2 4
5 Open Elective – III 3 0 2 4
6 CS 4191 Capstone Project — II 2 0 4 4
7 NU 312 Community Connect 0 0 2 1
Total semester L-T-P-C 15 0 20 25
# Course Code Course Title L T P C
1 NU402 Industry Practice / Project 0 0 4 20
Total semester L-T-P-C 0 0 4 20

Programme outcomes

NU’s undergraduate programmes in Engineering and Management are designed to provide thorough grounding in the respective disciplines, offer a course of work that prepares them for either a professional career or advanced degrees.
NU expects that graduates of the undergraduate Engineering programmes will demonstrate the following programme outcomes as defined by NBA (National Board of Accreditation).


Engineering knowledge

Apply the knowledge of Mathematics, Science, fundamentals of Engineering and an engineering specialisation to the solution of complex engineering problems.


Problem analysis

Identify, formulate, review research literature, and analyse complex engineering problems to reach substantiated conclusions using first principles of Mathematics, Natural Sciences, and Engineering Sciences.


Design/Develop solutions

Design solutions for complex engineering problems and system components or processes that meet specified needs with appropriate consideration for public health and safety, and cognisant of cultural, societal, and environmental considerations.


Conduct investigations of complex problems

Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesise information to provide valid conclusions.


Modern tool usage

Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.


The engineer and society

Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues, and the consequent responsibilities relevant to the professional engineering practice.


Environment and sustainability

Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.



Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.


Individual and teamwork

Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.



Communicate effectively on complex engineering activities with the engineering community and with society at large. This includes being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.


Project management and finance

Demonstrate knowledge and understanding of engineering and management principles and apply these to one’s own work, as a member and/or leader in a team, to manage projects in multidisciplinary environments.


Life-long learning

Recognise the need for, and have the preparation and ability to, engage in independent and life-long learning in the broadest context of technological change.


Programme Specific Outcomes for Data Science


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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