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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.
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:
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.
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
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
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 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 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 specific outcomes
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
and then