MCA (Master of Computer Applications) Data Science - NVIDIA
Duration2 Years
Course Fee3 Lakhs
The MCA in Data Science is a specialized postgraduate programme designed to develop advanced skills in data analysis, machine learning, and computational techniques.
The curriculum combines core computer science subjects with data-driven technologies such as statistics, data mining, big data analytics, and artificial intelligence. Students gain hands-on experience through practical labs, real-world datasets, and industry-oriented projects. The programme emphasizes problem-solving, predictive modeling, and data visualization to support informed decision-making. Graduates are well-prepared for careers in data science, analytics, research, and technology-driven industries.
Program Specific Outcomes (PSO)
PSO 01
To develop the technological planning and development of software applications in the usage of modern era.
PSO 02
To demonstrate the ability to simulate, analyze, design and deploy the software systems in advanced level for the industry and academic research.
Programme Outcomes (PO)
Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
Understand the impact of the 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.
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, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Recognize the need for and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Curriculum Details
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CAP4009/ CAP4009L | Programming and Problem Solving using Java | 3 | 0 | 2 | 4 |
| CAP3004 | Artificial Intelligence | 3 | 0 | 0 | 3 |
| MAT2003/MAT2003L | Probability and Statistics using Python/ Probability and Statistics using Python Lab | 3 | 0 | 2 | 4 |
| CAP4708 | Computer Organization and Architecture | 3 | 0 | 0 | 3 |
| Data Visualization with R/Data Visualization with R Lab | 3 | 0 | 2 | 4 | |
| GMT2761 | Principles of Management | 2 | 0 | 0 | 2 |
| CAP4008/CAP4008L | Software Engineering/Software Engineering Lab | 3 | 0 | 2 | 4 |
| Semester Credits | 24 |
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CAP4103/CAP4103L | Data Structures and Applications/ Data Structures and Applications Lab | 3 | 0 | 2 | 4 |
| Applied Data Analytics/Applied Data Analytics Lab | 3 | 0 | 2 | 4 | |
| CAP4005 | Computer Networks | 3 | 0 | 0 | 3 |
| DBMS with NO SQL/DBMS with NO SQL Lab | 3 | 0 | 2 | 4 | |
| CAP5006/CAP5006L | Operating System/Operating System Lab | 3 | 0 | 2 | 4 |
| CAP2016/CAP2016L | Full Stack Development/Full Stack Development Lab | 3 | 0 | 2 | 4 |
| Semester Credits | 23 |
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CAP5705/CAP5705L | Cryptography and Network Security/ Cryptography and Network Security Lab | 3 | 0 | 2 | 4 |
| CAP5707 | Data warehousing and Data Mining | 3 | 0 | 0 | 3 |
| CAP5007 | Cloud Computing | 3 | 0 | 0 | 3 |
| Big Data and Applications/Big Data and Applications Lab | 3 | 0 | 2 | 4 | |
| CAP4601 | Minor Project | 0 | 0 | 0 | 4 |
| Department Elective-I | 3 | 0 | 0 | 3 | |
| Department Elective-II | 3 | 0 | 0 | 3 | |
| Semester Credits | 24 |
| Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| CAP4502 | Major Project | 0 | 0 | 0 | 12 |
| Semester Credits | 12 |
| S.No | Course Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|---|
| 1 | CAP4707 | Introduction to Social Network Analysis | 3 | 0 | 0 | 3 |
| 2 | CSE2721 | Image Processing | 3 | 0 | 0 | 3 |
| 3 | CAP4704 | Big Data Technologies | 3 | 0 | 0 | 3 |
| 4 | CAP5706 | Artificial Neural Networks and Deep Learning | 3 | 0 | 0 | 3 |
| 5 | Essentials of Quantum Computing | 3 | 0 | 0 | 3 | |
| 6 | CSE3803 | Introduction to Data Science | 3 | 0 | 0 | 3 |
| 7 | CSE3704 | Software Testing Methodologies | 3 | 0 | 0 | 3 |
| 8 | CAP4705 | E-Commerce | 3 | 0 | 0 | 3 |
Career Path

- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Big Data Engineer
- AI Engineer
- Data Engineer
- Statistical Analyst
- Research Analyst
- Analytics Consultant
Fee Structure
Yearly
| 1st Year | 2nd Year |
|---|---|
| ₹1,65,000 | ₹1,35,000 |
Semester Wise
| 1st Sem | 2nd Sem | 3rd Sem | 4th Sem |
|---|---|---|---|
| ₹97,500 | ₹67,500 | ₹67,500 | ₹67,500 |
Admission Requirement
Passed BCA or equivalent Degree OR Passed B.Sc./ B.Com./ B.A. with Mathematics at 10+2 Level or at Graduation Level (with additional bridge Courses as per the norms of the University). Obtained at least 50% marks in the qualifying examination.