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 – Distinguishing Features
Our BTech Data Science programme is an advanced course that puts you into a high-growth journey. Here’s how:
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.
BTech Data Science – Course Outline
|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|
List of Professional Elective Courses in Data Science
BTech Data Science Course Syllabus & Structure
Year I (Semester I & Semester II)
|Science – I||3||0||2||4|
|EL 111||Fundamentals of Electronics||3||1||0||4|
|TA 111||Fundamentals of Computer Programming (Python)||2||0||4||4|
|TA 212||Workshop Practice||1||0||4||3|
|TA 102||Communication Skills||2||0||2||3|
|NU 111||Community Connect||0||0||2||1|
|Total semester L-T-P-C||14||2||12||22|
|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|
|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)
|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|
|7||NU 211||Community Connect||0||0||2||1|
|Total semester L-T-P-C||16||3||10||23|
|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|
|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)
|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|
|7||NU 311||Community Connect||0||0||2||1|
|Total semester L-T-P-C||17||0||12||23|
|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|
|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)
|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|
Apply the knowledge of Mathematics, Science, fundamentals of Engineering and an engineering specialisation to the solution of complex engineering problems.
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 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.
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