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BTech Data Science

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

NIIT University (NU) has been offering 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.

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 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 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 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 data science

BTech Data Science – Distinguishing Features

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 B Tech 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.
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 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 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.
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

Prof Debashis Sengupta
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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

BTech Data Science – Course Outline

Students at NU’s BTech 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 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

List of Professional Elective Courses in Data Science

01.
Social Media Analytics
02.
Cloud Computing Concepts
03.
Modeling & Simulation
04.
Multimodal data processing & analysis
05.
Numerical Methods for Data Science
06.
Dimensional and NoSQL Databases
07.
Cognitive Computing
08.
Data Stream Mining
09.
Data Integrity and Privacy
10.
Deep Learning
11.
Statistical Machine Learning
12.
Artificial Intelligence
13.
Data Mining
14.
Computer Vision
15.
Business Analytics
16.
Predictive Modelling for Data Science
17.
Big Data Concepts
18.
Artificial Neural Network
19.
Machine Learning
20.
Information Retrieval
21.
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

1

MAT 112 Calculus 3 1 0 4

2

Science – I 3 0 2 4

3

EL 111 Fundamentals of Electronics 3 1 0 4

4

TA 111 Fundamentals of Computer Programming (Python) 2 0 4 4

5

TA 212 Workshop Practice 1 0 4 3

6

TA 102 Communication Skills 2 0 2 3

7

NU 111 Community Connect 0 0 2 1
Total semester L-T-P-C 14 2 12 22
# 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 ENV 301 Environmental Science 3 0 0 3
4 CS 102 Data Structures (with Python) 3 0 2 4
5 TA 202 Engineering Graphics 2 0 2 3
6 HSSM-I 3 0 0 3
7 NU 111 Community Connect 0 0 2 1
Total semester L-T-P-C 17 1 6 21

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 Foundations of Data Science 3 0 2 4
3 CS 232 Discrete Mathematics 3 1 0 4
4 CS 251 Object Oriented Programming (with Java) 2 0 4 4
5 Data Visualisation 2 0 4 4
6 HSSM-II 3 0 0 3
7 NU 211 Community Connect 0 0 2 1
Total semester L-T-P-C 16 3 10 23
# Course code Course L T P C
1 CS 201 Design & Analysis of Algorithms 3 0 3 4
2 Statistical Methods for Data Science 3 0 2 4
3 Optimisation for Data Science 3 0 2 4
4 CS 211 Operating Systems 3 0 2 4
5 CS 231 Database Management Systems 3 0 2 4
6 HSSM-III 3 0 0 3
7 NU 212 Community Connect 0 0 2 1
Total semester L-T-P-C 18 0 11 23

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 Foundations of Machine Learning 3 0 2 4
2 CS 491 Natural Language Processing & Text Analytics 3 0 2 4
3 EL 302 Digital Image Processing 3 0 2 4
4 DS 412 Inferential Statistics for Data Science 2 0 4 4
5 Open Elective – I 3 0 2 4
6 HSSM-IV 3 0 0 3
7 NU 311 Community Connect 0 0 2 1
Total semester L-T-P-C 17 0 12 23
# Course code Course L T P C
1 Introduction to Artificial Intelligence & Deep Learning 3 0 2 4
2 Professional Elective – I 3 0 2 4
3 Professional Elective – II 3 0 2 4
4 CS 392 Capstone Project — I 1 0 6 4
5 NU 302 R & D Project 1 0 6 4
6 HSSM-V 3 0 0 3
7 NU 312 Community Connect 0 0 2 1
Total semester L-T-P-C 14 0 18 23

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 3 0 2 4
2 Professional Elective – IV 3 0 2 4
3 Professional Elective – V 3 0 2 4
4 Open Elective – II 3 0 2 4
5 Open Elective – III 3 0 2 4
6 Capstone Project — II 1 0 6 4
7 NU 312 Community Connect 0 0 2 1
Total semester L-T-P-C 16 0 16 24
# 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 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).

PO1

Engineering knowledge

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

PO2

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.

PO3

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.

PO4

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.

PO5

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.

PO6

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.

PO7

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.

PO8

Ethics

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

PO9

Individual and teamwork

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

PO10

Communication

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.

PO11

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.

PO12

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.

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Programme specific outcomes

Programme Specific Outcomes for Data Science

PSO1

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
PSO2
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
PSO3
Use modern tools, technologies, and programming languages in the area of Data science
PSO4
Apply the concepts and practical knowledge in analysis, design and development of data driven decision making systems and applications to solve multi-disciplinary problems
PSO5
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
PSO6
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
PSO7
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