MCA (Master of Computer Applications) Data Science - NVIDIA
Duration2 Years
Course Fee3 Lakhs
The MCA in Data Science programme focuses on extracting meaningful insights from structured and unstructured data using statistical analysis, machine learning, and computational techniques.
The programme prepares students to manage, analyze, and visualize data for strategic decision-making across 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 modelling 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 | C |
|---|---|---|
| CAP1005/CAP1005L | Programming and Problem Solving using C | 4 |
| CAP4103/CAP4103L | Data Structures and Applications | 4 |
| CAP4708 | Computer Organization and Architecture | 3 |
| CAP4102/CAP4102L | Advanced Database Management Systems/Advanced Database Management Systems Lab |
4 |
| Introduction to Data Science / Introduction to Data Science Lab | 2 | |
| CAP4008/CAP4008L | Software Engineering /Software Engineering Lab | 3 |
| Total Credits | 20 |
| Course Code | Course Title | C |
|---|---|---|
| MAT2003/MAT2003L | Probability and Statistics using Python | 4 |
| CAP4005/CAP4005L | Computer Networks and Security Essentials | 4 |
| Machine Learning and its Applications / Machine Learning and its Applications Lab | 3 | |
| CAP4009/CAP4009L | Java Programming | 4 |
| GMT2761 | Principles of Management | 2 |
| Data Visualization/Data Visualization Lab | 4 | |
| Total Credits | 21 |
| Course Code | Course Title | C |
|---|---|---|
| CAP5006/CAP5006L | Operating System | 4 |
| CAP5707 | Datawarehousing and Data Mining | 3 |
| CAP5007/CAP5007L | Cloud Computing | 4 |
| Business Analytics/Descriptive Analytics | 3 | |
| Departmental elective -2 | 3 | |
| Applied Data Analytics/Applied Data Analytics Lab | 4 | |
| CAP4601 | Minor Project | 5 |
| Total Credits | 26 |
| Course Code | Course Title | C |
|---|---|---|
| CAP4502 | Major Project | 18 |
| Total Credits | 18 |
| Course Code | Course Title | C |
|---|---|---|
| CAP4707 | Introduction to Social Network Analysis | 3 |
| CSE2721 | Image Processing | 3 |
| CAP4704 | Big Data Technologies | 3 |
| CAP5706 | Artificial Neural Networks and Deep Learning | 3 |
| CAP5705 | Cryptography and Network Security | 3 |
| CSE3803 | Introduction to Data Science | 3 |
| Quantum Computing | 3 | |
| CSE3704 | Software Testing Methodologies | 3 |
| CAP4705 | E-Commerce | 3 |
Career Path

Graduates can build careers as Data Scientists, applying statistical and machine learning techniques to extract actionable insights; Data Analysts, interpreting data to support informed decision-making; and Machine Learning Engineers, developing and deploying predictive models at scale. Opportunities also include roles as Business Intelligence Analysts, creating dashboards and reports for strategic planning; Big Data Engineers, managing and processing large-scale data systems; and Analytics Consultants, advising organizations on data-driven strategies and solutions.
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/ Bachelor Degree in Computer Science Engineering 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.