Course Structure Overview

The Data Science program at Chinmaya Vishwavidyapeeth is structured over eight semesters, with each semester comprising a carefully curated mix of core courses, departmental electives, science electives, and laboratory sessions. The curriculum balances theoretical knowledge with practical skills, ensuring that students are well-prepared for both industry roles and further academic pursuits.

Semester-wise Course Schedule

Year	Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
I	1	CS101	Programming Fundamentals	3-0-0-3	-
		CS102	Calculus I	4-0-0-4	-
		CS103	Linear Algebra	4-0-0-4	-
		CS104	Statistics & Probability	3-0-0-3	-
		CS105	Introduction to Data Science	2-0-0-2	-
		CS106	Computer Organization & Architecture	3-0-0-3	-
		CS107	Data Structures & Algorithms	4-0-0-4	CS101
		CS108	English for Technical Communication	2-0-0-2	-
		CS109	Electronics & Instrumentation Lab	0-0-3-1.5	-
		CS110	Programming Lab	0-0-3-1.5	CS101
I	2	CS201	Calculus II	4-0-0-4	CS102
		CS202	Differential Equations	3-0-0-3	CS102
		CS203	Data Structures & Algorithms II	4-0-0-4	CS107
		CS204	Database Systems	3-0-0-3	CS107
		CS205	Statistics for Data Analysis	3-0-0-3	CS104
		CS206	Probability & Stochastic Processes	3-0-0-3	CS104
		CS207	Introduction to Machine Learning	3-0-0-3	CS104
		CS208	Operating Systems	3-0-0-3	CS106
		CS209	Linear Programming & Optimization	3-0-0-3	CS103
		CS210	Database Systems Lab	0-0-3-1.5	CS204
II	3	CS301	Advanced Data Structures	3-0-0-3	CS203
		CS302	Deep Learning	3-0-0-3	CS207
		CS303	Natural Language Processing	3-0-0-3	CS207
		CS304	Data Visualization & Visualization Tools	3-0-0-3	CS205
		CS305	Statistical Inference	3-0-0-3	CS205
		CS306	Time Series Analysis	3-0-0-3	CS205
		CS307	Computer Vision	3-0-0-3	CS207
		CS308	Big Data Technologies	3-0-0-3	CS204
		CS309	Mathematical Modeling & Simulation	3-0-0-3	CS103
		CS310	Deep Learning Lab	0-0-3-1.5	CS302
II	4	CS401	Reinforcement Learning	3-0-0-3	CS302
		CS402	Cybersecurity Fundamentals	3-0-0-3	CS208
		CS403	Financial Modeling & Risk Analysis	3-0-0-3	CS205
		CS404	Healthcare Data Analytics	3-0-0-3	CS205
		CS405	Applied Machine Learning	3-0-0-3	CS302
		CS406	Cloud Computing & DevOps	3-0-0-3	CS208
		CS407	Research Methodology & Ethics	2-0-0-2	-
		CS408	Social Network Analysis	3-0-0-3	CS205
		CS409	Project Management & Innovation	2-0-0-2	-
		CS410	Cybersecurity Lab	0-0-3-1.5	CS402
III	5	CS501	Advanced Statistical Modeling	3-0-0-3	CS305
		CS502	Generative Adversarial Networks	3-0-0-3	CS302
		CS503	Explainable AI & Fairness	3-0-0-3	CS401
		CS504	Computational Biology	3-0-0-3	CS205
		CS505	Quantitative Finance	3-0-0-3	CS403
		CS506	Data Mining & Knowledge Discovery	3-0-0-3	CS304
		CS507	Privacy-Preserving Data Analysis	3-0-0-3	CS402
		CS508	Applied Deep Learning	3-0-0-3	CS302
		CS509	Environmental Data Science	3-0-0-3	CS306
		CS510	Advanced Analytics Lab	0-0-3-1.5	CS501
III	6	CS601	Special Topics in Data Science	3-0-0-3	CS501
		CS602	Research & Thesis Writing	2-0-0-2	-
		CS603	Internship & Industry Exposure	0-0-0-6	-
		CS604	Capstone Project I	0-0-6-3	-
		CS605	Capstone Project II	0-0-6-3	CS604
		CS606	Machine Learning & Optimization	3-0-0-3	CS302
		CS607	Data Governance & Compliance	3-0-0-3	CS402
		CS608	Neural Architecture Search	3-0-0-3	CS302
		CS609	Quantum Data Science	3-0-0-3	CS305
		CS610	Capstone Project Lab	0-0-3-1.5	CS605

Advanced Departmental Elective Courses

The program offers a wide range of advanced departmental electives designed to deepen students' understanding of specialized areas within Data Science:

• Generative Adversarial Networks (GANs): This course delves into the architecture and applications of GANs, covering topics such as style transfer, super-resolution, and anomaly detection. Students gain hands-on experience in developing novel generative models using frameworks like TensorFlow and PyTorch.
• Explainable Artificial Intelligence (XAI): This course focuses on making machine learning models interpretable and transparent. Topics include LIME, SHAP, attention mechanisms, and ethical implications of AI decisions. Students learn how to build trustworthy systems that can explain their outputs clearly to stakeholders.
• Computational Biology: This course bridges the gap between biology and data science by applying computational methods to understand biological processes. It covers genomics, proteomics, protein structure prediction, and bioinformatics tools used in drug discovery and personalized medicine.
• Privacy-Preserving Data Analysis: Students learn techniques for analyzing sensitive datasets without compromising individual privacy. The course explores differential privacy, homomorphic encryption, secure multi-party computation, and federated learning strategies.
• Data Governance & Compliance: This elective introduces students to regulatory frameworks governing data usage, including GDPR, HIPAA, and CCPA. It covers compliance auditing, risk assessment, and implementing governance policies in enterprise environments.
• Quantum Data Science: An emerging field that combines quantum computing with data analysis. The course explores quantum algorithms for optimization, classification, and simulation tasks. Students experiment with quantum simulators and explore real-world applications in machine learning and cryptography.
• Neural Architecture Search (NAS): This course investigates automated methods for designing neural networks. It covers evolutionary algorithms, reinforcement learning-based NAS, and differentiable architecture search techniques. Practical implementation using AutoML frameworks is emphasized.
• Applied Deep Learning: Focused on real-world applications of deep learning models in domains such as computer vision, natural language processing, and speech recognition. Students work on projects involving image classification, sentiment analysis, and voice synthesis.
• Machine Learning & Optimization: This course explores optimization techniques used in machine learning, including gradient descent, stochastic optimization, and convex optimization. It also covers regularization methods, hyperparameter tuning, and ensemble learning strategies.
• Social Network Analysis: Students analyze large-scale social networks using graph theory and network science principles. The course covers community detection, centrality measures, influence propagation, and link prediction algorithms in platforms like Facebook, Twitter, and LinkedIn.

Project-Based Learning Philosophy

The Data Science program at Chinmaya Vishwavidyapeeth emphasizes project-based learning as a core component of the curriculum. This pedagogical approach ensures that students apply theoretical concepts to real-world problems, fostering innovation and problem-solving skills.

Mini-projects are assigned throughout each semester, starting from the first year and building up in complexity. These projects typically involve working in teams of 3-5 students on datasets provided by industry partners or academic institutions. Students are required to present their findings to faculty members and peers, receiving constructive feedback that helps refine their analytical capabilities.

The final-year capstone project is a significant undertaking that spans the entire semester. Students select an independent research topic under the supervision of a faculty advisor. They are expected to conduct original research, develop novel methodologies, and deliver a comprehensive report along with a live demonstration or prototype. The project must demonstrate mastery in data science techniques and contribute value to the field or industry.

Evaluation criteria for both mini-projects and the capstone project include:

• Problem identification and definition
• Data collection and preprocessing methods
• Methodology selection and implementation
• Results interpretation and validation
• Communication of findings through written reports and presentations
• Originality and innovation in approach
• Technical proficiency and tool usage
• Adherence to ethical standards and reproducibility

Students are encouraged to collaborate with external organizations, including startups, NGOs, and government agencies, to address real-world challenges. This exposure not only enhances their technical skills but also develops soft skills such as teamwork, communication, and project management.