Master of Science (Data Science)


Program Info (  Eligibility Criteria  )

This program equips students to transform data into actionable insights that enable one to make complex business decisions. Students will able to process large and complex data sets through computational, statistical, and machine learning techniques. This program will provide exposure to the latest trends and technologies in this field. Thus, producing the man power to fuel national and international emerging market of data science products.

Tentative Study Plan
Sr. No Course Name Crdt Hrs.
Semester 1
1 Applied Programming 1 NC
2 Data Science Tools & Techniques 3+0
3 Statistical & Mathematical 3+0
4 Methods for Data Science
5 Specialized Core-I 3+0
Sr. No Course Name Crdt Hrs.
Semester 2
1 Machine Learning for Data Science 3+0
2 Specialized Core-II 3+0
3 Research Methodology 3+0
Sr. No Course Name Crdt Hrs.
Semester 3
1 Computing Elective-I 3+0
2 MS Thesis-I/MS Project-I 0+3
Sr. No Course Name Crdt Hrs.
Semester 4
1 Computing Elective-II 3+0
2 MS Thesis-II/MS Project-II 0+3

Note 1: Applied Programming course is of No Credit (NC), but it must be passed.

Note 2: Registration in “MS Thesis - I” is allowed provided the student has:

  • Earned at least 15 credits
  • Passed the “Research Methodology” course
  • CGPA is equal to or more than 2.5
Specialized Core Courses
  • DS 5001  Advance Big Data Analytics
  • DS 5006  Deep Learning
  • DS 5007  Natural Language Processing
  • DS 5005  Distributed Data Processing

Program Educational Objectives (PEO)

  1. To produce computer scientists who fulfil the requirements of the national and international market of data science products.
  2. To equip students to transform data into actionable insights that enable them to make complex business decisions.
  3. To enable students to apply computational, statistical, and machine learning techniques to process large and complex data sets.
  4. To enable students to conceive and execute data science projects.

Program Learning Outcomes (PLOs)

1

Knowledge of Data Science

Have an advanced, and coherent disciplinary and interdisciplinary knowledge of Data Science technologies, and research principles and methods.

2

Critical Thinking, Design Thinking and Decision-Making Skills

Develop problem solving, design and decision-making skills to identify and provide innovative solutions to complex Data problems through application of related technologies and techniques.

3

Ethics and Social Responsibility

Demonstrate mindfulness of professional practices in a global and sustainable context and act with professional accountability and integrity.

4

Research Methods Competence

Apply knowledge of research principles and methods to plan and execute a research-based practical project with personal autonomy and accountability.

5

Communication Skills

Interpret, document and present the core issues, problem statements, evaluation reviews, requirements and findings in developing Data Science research work.