Comprehensive Course Structure
The Data Science program at Bipin Tripathi Kumaon Institute Of Technology is structured over eight semesters, with a blend of core subjects, departmental electives, science electives, and laboratory sessions designed to provide students with a holistic understanding of data science principles and practices.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | DS101 | Introduction to Data Science | 3-0-0-3 | None |
| 1 | DS102 | Mathematics for Data Science I | 4-0-0-4 | None |
| 1 | DS103 | Programming Fundamentals | 3-0-0-3 | None |
| 1 | DS104 | Statistics for Data Science | 3-0-0-3 | None |
| 1 | DS105 | Lab: Programming and Tools | 0-0-2-1 | DS103 |
| 2 | DS201 | Mathematics for Data Science II | 4-0-0-4 | DS102 |
| 2 | DS202 | Data Structures and Algorithms | 3-0-0-3 | DS103 |
| 2 | DS203 | Database Management Systems | 3-0-0-3 | None |
| 2 | DS204 | Probability and Inference | 3-0-0-3 | DS104 |
| 2 | DS205 | Lab: Data Structures and Algorithms | 0-0-2-1 | DS202 |
| 3 | DS301 | Machine Learning Fundamentals | 3-0-0-3 | DS201, DS204 |
| 3 | DS302 | Data Mining Techniques | 3-0-0-3 | DS204 |
| 3 | DS303 | Statistical Inference and Modeling | 3-0-0-3 | DS104, DS201 |
| 3 | DS304 | Data Visualization and Communication | 3-0-0-3 | DS104 |
| 3 | DS305 | Lab: Machine Learning Applications | 0-0-2-1 | DS301 |
| 4 | DS401 | Deep Learning and Neural Networks | 3-0-0-3 | DS301, DS302 |
| 4 | DS402 | Natural Language Processing | 3-0-0-3 | DS301 |
| 4 | DS403 | Time Series Analysis and Forecasting | 3-0-0-3 | DS303 |
| 4 | DS404 | Advanced Statistical Methods | 3-0-0-3 | DS303 |
| 4 | DS405 | Lab: Deep Learning and NLP | 0-0-2-1 | DS401, DS402 |
| 5 | DS501 | Data Engineering and Big Data | 3-0-0-3 | DS302 |
| 5 | DS502 | Financial Data Science | 3-0-0-3 | DS303 |
| 5 | DS503 | Healthcare Analytics | 3-0-0-3 | DS301, DS303 |
| 5 | DS504 | Geospatial Data Science | 3-0-0-3 | DS302 |
| 5 | DS505 | Lab: Specialized Applications | 0-0-2-1 | DS501, DS502 |
| 6 | DS601 | Research Methodology | 3-0-0-3 | DS401, DS501 |
| 6 | DS602 | Advanced Topics in Data Science | 3-0-0-3 | DS501 |
| 6 | DS603 | Industry Internship | 0-0-0-3 | DS501, DS502 |
| 6 | DS604 | Capstone Project I | 0-0-0-3 | DS601 |
| 7 | DS701 | Advanced Capstone Project II | 0-0-0-6 | DS604 |
| 7 | DS702 | Professional Development | 3-0-0-3 | None |
| 7 | DS703 | Entrepreneurship in Data Science | 3-0-0-3 | None |
| 8 | DS801 | Final Year Thesis/Project | 0-0-0-6 | DS701 |
| 8 | DS802 | Internship Report and Presentation | 0-0-0-3 | DS603 |
Advanced Departmental Elective Courses
These advanced elective courses are designed to provide depth in specialized areas of data science, enabling students to tailor their education according to their interests and career goals.
1. Advanced Machine Learning Algorithms
This course delves into cutting-edge machine learning models such as ensemble methods, boosting algorithms, generative adversarial networks (GANs), and reinforcement learning. Students will implement these models using Python frameworks like TensorFlow and PyTorch, gaining hands-on experience with complex datasets.
2. Ethical Data Science
Exploring the ethical implications of data collection, processing, and interpretation, this course addresses privacy concerns, bias in algorithms, and regulatory compliance. It prepares students to navigate the moral landscape of modern data science practices responsibly.
3. Quantum Computing for Data Science
Introducing quantum computing concepts and their applications in data science, this course explores how quantum algorithms can revolutionize optimization problems and machine learning tasks. Students will experiment with quantum simulators and understand the potential future impact of quantum technologies.
4. Computational Social Science
Using large-scale social media data, this course investigates human behavior through computational methods. Topics include network analysis, sentiment classification, influence propagation, and social dynamics modeling using real-world datasets.
5. Computer Vision and Image Processing
Focusing on image recognition, object detection, and facial recognition systems, this course combines theoretical knowledge with practical implementation using libraries like OpenCV and scikit-image. It explores applications in autonomous vehicles, medical imaging, and surveillance systems.
6. Recommender Systems
Students learn to design and evaluate recommendation algorithms used by platforms like Netflix, Spotify, and Amazon. The course covers collaborative filtering, content-based filtering, hybrid methods, and deep learning approaches for personalized recommendations.
7. Data Privacy and Security
This course examines cryptographic techniques, secure multi-party computation, differential privacy, and anonymization strategies. It equips students with the knowledge needed to protect sensitive data while extracting valuable insights.
8. Time Series Forecasting
Building upon foundational statistical methods, this course explores advanced forecasting models including ARIMA, GARCH, LSTM-based approaches, and state-space models. Applications in financial markets, climate modeling, and supply chain management are discussed.
9. Natural Language Understanding
Advanced NLP techniques such as transformers, BERT, RoBERTa, and language modeling are covered in this course. Students will work with pre-trained models and fine-tune them for specific tasks like question answering, summarization, and text generation.
10. Data Governance and Quality Assurance
This course emphasizes the importance of maintaining data integrity and quality within organizations. Topics include metadata management, data validation techniques, audit trails, and governance frameworks that ensure compliance with industry standards.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a core component of the curriculum. Through structured mini-projects and capstone initiatives, students are encouraged to apply theoretical knowledge to solve real-world problems.
Mini-Projects
Mini-projects are assigned at regular intervals throughout each semester to reinforce classroom concepts. These projects typically involve small teams (3-5 members) working under faculty supervision. Each project is evaluated based on technical execution, creativity, presentation quality, and adherence to deadlines.
Final-Year Thesis/Capstone Project
The capstone project serves as the culmination of the undergraduate experience. Students select a topic aligned with their interests or industry needs, conduct extensive research, develop prototypes, and present findings to a panel of experts. The project is supervised by a faculty mentor who provides guidance throughout the process.
Project Selection Process
Students are encouraged to propose project ideas in consultation with faculty advisors. The selection process involves a proposal submission, peer review, and final approval by the department head. Projects can be individual or team-based, depending on complexity and scope.