Master of Science (Data Science)
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.
| 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
- DS 5001 Advance Big Data Analytics
- DS 5006 Deep Learning
- DS 5007 Natural Language Processing
- DS 5005 Distributed Data Processing
Program Educational Objectives (PEO)
- To produce computer scientists who fulfil the requirements of the national and international market of data science products.
- To equip students to transform data into actionable insights that enable them to make complex business decisions.
- To enable students to apply computational, statistical, and machine learning techniques to process large and complex data sets.
- 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. |