*Semester 1: Foundation Basics* 

1. *Mathematics-I* (Calculus, Linear Algebra) 

2. *Programming Fundamentals* (Python, C/C++) 

3. *Engineering Physics* (Basics of Computing Hardware) 

4. *Introduction to Data Science* (Data Lifecycle, Applications) 

5. *Digital Logic Design* (Boolean Algebra, Logic Gates) 

6. *Environmental Science* 

*Lab*: Python Programming Lab, Basic Electronics Lab 

 

*Semester 2: Core Programming & Math* 

1. *Mathematics-II* (Probability, Statistics) 

2. *Data Structures and Algorithms* (Arrays, Trees, Graphs) 

3. *Database Management Systems* (SQL, Relational Algebra) 

4. *Discrete Mathematics* (Sets, Logic, Combinatorics) 

5. *Technical Communication* 

*Lab*: SQL Lab, Data Structures Implementation (Python/C++) 

 

*Semester 3: Data Science Fundamentals* 

1. *Mathematics-III* (Multivariate Calculus, Optimization) 

2. *Object-Oriented Programming* (Java/C++) 

3. *Statistical Methods for Data Science* (Hypothesis Testing, Regression) 

4. *Operating Systems* (Process Management, File Systems) 

5. *Data Visualization* (Matplotlib, Tableau, Power BI) 

*Lab*: Statistical Analysis (R/Python), Visualization Projects 

 

*Semester 4: Machine Learning & Engineering* 

1. *Introduction to Machine Learning* (Supervised/Unsupervised Learning) 

2. *Big Data Technologies* (Hadoop, Spark, MapReduce) 

3. *Web Technologies* (APIs, RESTful Services) 

4. *Linear Algebra for Data Science* (Matrix Operations, Eigenvalues) 

5. *Software Engineering* (Agile, DevOps Basics) 

*Lab*: ML with Scikit-learn, Hadoop/Spark Cluster Setup 

 

 

 *Semester 5: Advanced Analytics & Systems* 

1. *Deep Learning* (Neural Networks, TensorFlow/PyTorch) 

2. *Data Engineering* (ETL Pipelines, Airflow, Kafka) 

3. *Cloud Computing* (AWS/Azure/GCP, Serverless Architectures) 

4. *Time Series Analysis* (ARIMA, Forecasting) 

5. *Elective-I* (e.g., Natural Language Processing) 

*Lab*: Cloud Deployment Lab, End-to-End ML Pipeline Projects 

 

*Semester 6: Big Data & AI Applications* 

1. *Big Data Analytics* (Hive, HBase, NoSQL Databases) 

2. *Reinforcement Learning* (Q-Learning, Policy Gradients) 

3. *Distributed Systems* (Scalability, Consistency Models) 

4. *Business Intelligence* (Dashboarding, Decision Trees) 

5. *Elective-II* (e.g., Computer Vision) 

*Lab*: Real-Time Analytics (Kafka Streams), AI Model Deployment 

 

*Semester 7: Specialization & Capstone Projects* 

1. *AI Ethics and Governance* (Bias, Fairness, GDPR) 

2. *Advanced Data Engineering* (Data Lakes, Delta Lake) 

3. *Elective-III* (e.g., Blockchain for Data Security) 

4. *Elective-IV* (e.g., IoT Data Analytics) 

5. *Capstone Project-I* (Industry Problem Solving, e.g., Predictive Maintenance) 

*Lab*: MLOps (CI/CD Pipelines), Ethical AI Auditing Tools 

 

*Semester 8: Industry Integration* 

1. *Industrial Internship* (6–8 Weeks in Data-Driven Firms) 

2. *Capstone Project-II* (Thesis on Real-World Dataset, e.g., Healthcare Analytics) 

3. *Professional Ethics* 

4. *Elective-V* (e.g., Quantum Machine Learning) 

 

 *Electives (Sample)* 

- *Advanced NLP* (Transformers, BERT) 

- *Robotic Process Automation (RPA)* 

- *Financial Data Analytics* 

- *Geospatial Data Science* 

- *Recommender Systems*