Comprehensive Course Structure
The curriculum for the B.Tech in Data Science is structured over eight semesters, with a balanced mix of foundational subjects, core engineering principles, and specialized electives. The program ensures that students gain both breadth and depth in their understanding of data science while developing practical skills through laboratory sessions, mini-projects, and capstone research.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | None |
| 1 | MAT101 | Calculus I | 4-0-0-4 | None |
| 1 | STA101 | Probability and Statistics | 3-0-0-3 | None |
| 1 | CS102 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 1 | MAT102 | Linear Algebra | 3-0-0-3 | None |
| 1 | ENG101 | English Communication | 2-0-0-2 | None |
| 2 | CS201 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| 2 | MAT201 | Calculus II | 4-0-0-4 | MAT101 |
| 2 | STA201 | Statistical Inference | 3-0-0-3 | STA101 |
| 2 | CS202 | Database Systems | 3-0-0-3 | CS102 |
| 2 | CS203 | Computer Organization and Architecture | 3-0-0-3 | None |
| 2 | MAT202 | Discrete Mathematics | 3-0-0-3 | None |
| 3 | CS301 | Machine Learning Fundamentals | 3-0-0-3 | CS201, MAT201, STA201 |
| 3 | CS302 | Big Data Technologies | 3-0-0-3 | CS202 |
| 3 | CS303 | Data Mining and Warehousing | 3-0-0-3 | CS202, STA201 |
| 3 | CS304 | Statistical Modeling | 3-0-0-3 | MAT201, STA201 |
| 3 | CS305 | Software Engineering | 3-0-0-3 | CS201 |
| 3 | MAT301 | Optimization Techniques | 3-0-0-3 | MAT201 |
| 4 | CS401 | Deep Learning and Neural Networks | 3-0-0-3 | CS301, MAT301 |
| 4 | CS402 | Natural Language Processing | 3-0-0-3 | CS301, CS304 |
| 4 | CS403 | Data Visualization and Reporting | 3-0-0-3 | STA201, CS303 |
| 4 | CS404 | Reinforcement Learning | 3-0-0-3 | CS301, MAT301 |
| 4 | CS405 | Cybersecurity in Data Science | 3-0-0-3 | CS203, CS302 |
| 4 | MAT401 | Advanced Probability and Stochastic Processes | 3-0-0-3 | MAT201, MAT202 |
| 5 | CS501 | Time Series Analysis | 3-0-0-3 | STA201, MAT301 |
| 5 | CS502 | Computational Biology | 3-0-0-3 | CS304, MAT301 |
| 5 | CS503 | Financial Engineering and Risk Analytics | 3-0-0-3 | STA201, MAT301 |
| 5 | CS504 | Advanced Data Mining Techniques | 3-0-0-3 | CS303 |
| 5 | CS505 | Big Data Analytics with Hadoop and Spark | 3-0-0-3 | CS302, CS401 |
| 5 | MAT501 | Mathematical Optimization for AI | 3-0-0-3 | MAT301 |
| 6 | CS601 | Industry Internship I | 2-0-0-2 | CS501, CS504 |
| 6 | CS602 | Capstone Project - Phase I | 3-0-0-3 | CS401, CS501 |
| 7 | CS701 | Industry Internship II | 2-0-0-2 | CS601 |
| 7 | CS702 | Capstone Project - Phase II | 3-0-0-3 | CS602 |
| 8 | CS801 | Final Year Thesis | 4-0-0-4 | CS702 |
| 8 | CS802 | Professional Development and Ethics in Data Science | 2-0-0-2 | None |
Detailed Course Descriptions for Departmental Electives
The department offers a wide range of advanced elective courses that allow students to specialize in their chosen areas within data science. These courses are designed by leading faculty members and align with current industry trends and research advancements.
Advanced Deep Learning: This course explores modern architectures such as Transformers, GANs, and Autoencoders, providing students with hands-on experience using frameworks like TensorFlow and PyTorch. The focus is on developing models for complex tasks such as image generation, natural language understanding, and reinforcement learning.
Natural Language Processing: Students learn about text preprocessing, language modeling, sentiment analysis, machine translation, and dialogue systems. This course utilizes cutting-edge tools like BERT, RoBERTa, and Hugging Face Transformers to build intelligent applications for processing human language.
Reinforcement Learning: The course covers Q-learning, policy gradients, actor-critic methods, and multi-agent systems. Students apply these concepts in simulations and real-world environments using platforms like OpenAI Gym and MuJoCo.
Cybersecurity for Data Science: This course introduces students to the intersection of data science and cybersecurity, focusing on threat detection, privacy-preserving analytics, and secure data handling practices. Students learn to identify vulnerabilities in data pipelines and implement robust defense mechanisms.
Financial Engineering and Risk Analytics: The curriculum covers quantitative modeling, portfolio optimization, derivatives pricing, and risk management. Students use financial datasets to simulate market scenarios and develop predictive models for asset valuation and investment strategies.
Data Visualization and Reporting: This course emphasizes visual storytelling through tools like Tableau, Power BI, and D3.js. Students learn how to design dashboards, create interactive reports, and communicate insights effectively to stakeholders across different domains.
Big Data Analytics with Hadoop and Spark: Students explore distributed computing frameworks for processing large-scale datasets. Topics include MapReduce, YARN, Spark SQL, and streaming analytics using Kafka and Storm.
Computational Biology: This course applies data science techniques to biological problems such as gene expression analysis, protein structure prediction, and drug discovery. Students gain experience with bioinformatics tools and databases like UniProt and NCBI.
Advanced Data Mining Techniques: The course delves into clustering algorithms, association rule mining, anomaly detection, and recommendation systems. Students learn to implement these techniques on real-world datasets using Python libraries such as Scikit-learn and MLlib.
Time Series Analysis: This course focuses on forecasting models for temporal data, including ARIMA, SARIMA, LSTM networks, and seasonal decomposition methods. Applications include economic forecasting, weather prediction, and stock market analysis.
Mathematical Optimization for AI: The course covers linear programming, convex optimization, and integer programming techniques used in machine learning. Students apply these methods to solve complex problems in AI and robotics.
Project-Based Learning Philosophy
The department believes that project-based learning is essential for developing practical skills and deepening understanding of data science concepts. The program includes mandatory mini-projects in the second and third years, followed by a comprehensive final-year thesis or capstone project.
Mini-projects are designed to be collaborative efforts that simulate real-world challenges. Students form teams of 3–5 members and work on a problem assigned by faculty or industry partners. These projects span several weeks and require students to apply multiple skills from the curriculum, including data preprocessing, model development, evaluation metrics, and presentation.
The capstone project is an extended research endeavor that allows students to explore a topic of personal interest in depth. Students select their topics with guidance from faculty mentors and develop original solutions or methodologies. The final project is presented at a national symposium and may lead to publication opportunities or patent applications.
Projects are evaluated based on several criteria including technical proficiency, creativity, teamwork, communication skills, and impact. Feedback from industry experts and faculty members ensures continuous improvement in student outcomes.