Curriculum Overview
The Data Science program at Universal Ai University Maharashtra is designed to provide students with a comprehensive foundation in mathematics, statistics, programming, and domain-specific applications. The curriculum is structured over eight semesters, combining core courses, departmental electives, science electives, and laboratory sessions.
Semester-wise Course Structure
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus and Differential Equations | 3-0-0-3 | None |
| 1 | MATH102 | Linear Algebra and Matrices | 3-0-0-3 | None |
| 1 | CS101 | Introduction to Programming (Python) | 2-0-2-3 | None |
| 1 | STAT101 | Probability and Statistics | 3-0-0-3 | MATH101 |
| 1 | CS102 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| 1 | ENGL101 | English for Technical Communication | 2-0-0-2 | None |
| 2 | MATH201 | Advanced Calculus and Vector Analysis | 3-0-0-3 | MATH101 |
| 2 | STAT201 | Statistical Inference and Estimation | 3-0-0-3 | STAT101 |
| 2 | CS201 | Database Systems | 3-0-2-4 | CS102 |
| 2 | ML101 | Introduction to Machine Learning | 3-0-0-3 | STAT101, CS102 |
| 2 | CS202 | Computer Graphics and Visualization | 2-0-2-3 | CS101 |
| 2 | ENG201 | Technical Writing and Presentation Skills | 2-0-0-2 | ENGL101 |
| 3 | ML201 | Deep Learning and Neural Networks | 3-0-0-3 | ML101 |
| 3 | STAT301 | Time Series Analysis and Forecasting | 3-0-0-3 | STAT201 |
| 3 | CS301 | Data Mining and Big Data Analytics | 3-0-2-4 | CS201, ML101 |
| 3 | ML301 | Natural Language Processing | 3-0-0-3 | ML101, STAT201 |
| 3 | CS302 | Cloud Computing and Distributed Systems | 3-0-2-4 | CS201 |
| 3 | CS303 | Computer Vision and Image Processing | 3-0-2-4 | CS202 |
| 4 | ML401 | Reinforcement Learning | 3-0-0-3 | ML201, STAT301 |
| 4 | STAT401 | Causal Inference and Experimental Design | 3-0-0-3 | STAT201 |
| 4 | CS401 | Special Topics in Data Science | 3-0-0-3 | CS301, ML201 |
| 4 | CS402 | Capstone Project I | 0-0-6-6 | All previous courses |
| 5 | ML501 | Advanced Deep Learning Architectures | 3-0-0-3 | ML401, CS303 |
| 5 | CS501 | Data Engineering and Pipeline Design | 3-0-2-4 | CS301, CS302 |
| 5 | CS502 | Cybersecurity for Data Science | 3-0-0-3 | CS301, CS302 |
| 5 | CS503 | Quantitative Finance and Risk Modeling | 3-0-0-3 | STAT401, ML201 |
| 5 | CS504 | Healthcare Data Science | 3-0-0-3 | STAT201, ML101 |
| 5 | CS505 | Social Media Analytics and User Behavior Modeling | 3-0-0-3 | ML101, STAT201 |
| 6 | CS601 | Capstone Project II | 0-0-6-6 | CS402, CS501 |
| 6 | CS602 | Research Methodology in Data Science | 3-0-0-3 | ML401, STAT401 |
| 6 | CS603 | Internship Preparation and Industry Exposure | 2-0-0-2 | CS401, CS501 |
| 7 | CS701 | Specialized Electives in AI/ML | 3-0-0-3 | ML501, CS501 |
| 7 | CS702 | Computational Biology and Genomics | 3-0-0-3 | ML401, STAT401 |
| 7 | CS703 | Ethics in Data Science and AI | 2-0-0-2 | ML401, CS502 |
| 7 | CS704 | Industry Project and Collaboration | 0-0-6-6 | CS601, CS602 |
| 8 | CS801 | Final Year Thesis and Research | 0-0-6-6 | All previous courses |
Advanced Departmental Electives
These courses provide students with advanced knowledge and skills in specialized areas of data science:
Deep Learning and Neural Networks (ML201)
This course delves into the architecture and training of deep neural networks, including convolutional, recurrent, and transformer-based models. Students learn to implement complex architectures using frameworks like TensorFlow and PyTorch.
Natural Language Processing (ML301)
Students explore text processing techniques, sentiment analysis, language modeling, and machine translation. The course includes hands-on projects involving large language models and their applications in real-world scenarios.
Causal Inference and Experimental Design (STAT401)
This advanced course focuses on understanding causality through statistical methods and experimental design principles. It prepares students to analyze observational data and draw valid causal conclusions.
Computer Vision and Image Processing (CS303)
The course covers image recognition, object detection, segmentation techniques, and generative models. Students work with datasets like CIFAR-10 and ImageNet, implementing CNNs and GANs for various computer vision tasks.
Reinforcement Learning (ML401)
This course introduces reinforcement learning agents, Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Practical implementation is emphasized through simulations and real-world problem-solving.
Data Engineering and Pipeline Design (CS501)
Students learn to design scalable data pipelines using technologies like Apache Spark, Kafka, and Hadoop. The course includes designing data warehouses and optimizing data flow for enterprise-level applications.
Cybersecurity for Data Science (CS502)
This course explores how to protect data assets during analysis and modeling. Topics include encryption, access control, anomaly detection, and secure coding practices in data science environments.
Quantitative Finance and Risk Modeling (CS503)
Students study financial markets, derivatives pricing, portfolio optimization, and risk management models. The course includes practical sessions using Python libraries for quantitative analysis.
Healthcare Data Science (CS504)
This elective focuses on applying data science to medical research, electronic health records, drug discovery, and clinical trial analysis. Students work with real healthcare datasets and learn to interpret results within regulatory frameworks.
Social Media Analytics and User Behavior Modeling (CS505)
The course covers user engagement metrics, content propagation models, recommendation systems, and social network analysis. Students use tools like Gephi and NetworkX to visualize and analyze complex networks.
Project-Based Learning
Our program emphasizes project-based learning throughout the curriculum. Students begin with mini-projects in early semesters, progressing to larger capstone projects in their final years. Projects are selected based on student interests and aligned with faculty research areas.
Mini-projects are typically completed within 2–3 months and involve working in teams of 3–5 students. These projects are evaluated using rubrics that consider technical execution, presentation quality, and innovation.
The final-year thesis/capstone project is a significant endeavor lasting 6–8 months. Students collaborate closely with faculty mentors to define research questions, design experiments, implement solutions, and present findings at departmental symposiums.