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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

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.