This is a two-year, four-semester course that presents balanced blend of Machine Learning, Deep Learning, Data Analytics, Statistics, and Mathematics. Providing students with the unique opportunity to tackle data-intensive challenges in industries like Healthcare, Manufacturing, Humanity, and IT, by making sense of data and generating meaningful insights. Demonstration of end-to-end case studies showcasing application of Data Science in a particular domain by experts. Completing an MSc in Data Science, students are sought in various industries for their ability to extract valuable insights from data and contribute to the decision-making processes.
Any graduate having (Statistics / Mathematics / Computer Science / Data Science as one of the subjects at graduation) with 50% marks for General and 45% marks for Reserved Class category candidate belonging to Maharashtra State only.
Any graduate having (Statistics / Mathematics / Computer Science / Data Science as one of the subjects at graduation) with 50% marks
The selection procedure is based on marks obtained in Remote Proctored Online DES PU PCET; Data Science 2026 exam.
The curriculum focusses on introduction to the basics of Mathematics and Statistics with respect to Data Science, Introduction to Data Management concepts and various Data Science aspects like Machine Learning and Deep Learning.
Subjects like "Emerging Trends" provide students with in-depth, hands-on experience related to the latest tools and technologies used in the industry, giving them a competitive edge.
Subjects like Research Methodology and Deep Learning facilitate students to explore research aspects in the field of Data Science.
We prioritise hands-on laboratory courses in our curriculum. Subjects like SQL for Data Science, Python follow an Experimental/Experiential Learning approach, allowing students to easily gain expertise in practical knowledge.
“Data Science Story Telling and Visualization” and “Data Science case studies” encourage and equip students to apply technology in a given case study.
Project work helps students to explore various domains in Data Science, and in designing end-to-end working models.
(NEP Compliant)
| Sr No | Semester 1 |
|---|---|
| 1 | Python Programming |
| 2 | Statistics for Data Science |
| 3 | Mathematical Foundation |
| 4 | Data Structure and Analysis of Algorithm |
| 5 | Research Methodology |
| 6 | Practical Python Programming |
| 7 | SQL for Data Science (Experiential Learning) |
| 8 | Skill Development – I |
| 9 | Professional Enhancement – I |
| Sr No | Semester 2 |
|---|---|
| 1 | Machine Learning |
| 2 | Statistical Inference |
| 3 | Soft Computing |
| 4 | Optimization Techniques OR |
| 5 | Data Integration and Data Warehousing |
| 6 | On-Job Training / Field Project |
| 7 | Practical Machine Learning |
| 8 | Data Storytelling and Visualization |
| 9 | Skill Development – II |
| 10 | Professional Enhancement – II |
| Sr No | Semester 3 |
|---|---|
| 1 | Data Science Case Studies |
| 2 | Deep Learning |
| 3 | Natural Language Processing |
| 4 | Software Development and Project Management |
| 5 | Big Data Analytics OR |
| 6 | Time series analysis and forecasting |
| 7 | Research Project |
| 8 | Practical on Theory subjects |
| 9 | Emerging Tools and Techniques in Data Science (Experiential Learning) |
| 10 | Skill Development-III |
| 11 | Professional Enhancement -IV |
| Sr No | Semester 4 |
|---|---|
| 1 | MOOCS |
| 2 | Internship |
Data Scientist.
Machine Learning Engineer.
Data Analyst.
Data Engineer.
Business Intelligence (BI) Analyst.
Data Architect.
Data Science Consultant.
AI Research Scientist.
