Comprehensive Course Catalog

Semester	Course Code	Course Title	Credits (L-T-P-C)	Prerequisites
1	CS101	Mathematics for Computer Science	3-1-0-4	None
1	CS102	Introduction to Programming	3-0-2-5	None
1	CS103	Engineering Graphics	2-0-2-4	None
1	CS104	Physical Sciences	3-1-0-4	None
1	CS105	Communication Skills	2-0-0-2	None
2	CS201	Data Structures and Algorithms	3-1-0-4	CS102
2	CS202	Database Systems	3-1-0-4	CS102
2	CS203	Operating Systems	3-1-0-4	CS102
2	CS204	Computer Networks	3-1-0-4	CS201
2	CS205	Web Technologies	3-0-2-5	CS102
3	CS301	Software Engineering	3-1-0-4	CS201, CS202
3	CS302	Machine Learning	3-1-0-4	CS201
3	CS303	Cybersecurity Fundamentals	3-1-0-4	CS204
3	CS304	Data Mining and Analytics	3-1-0-4	CS201
3	CS305	Human Computer Interaction	3-1-0-4	CS201
4	CS401	Advanced Algorithms	3-1-0-4	CS201
4	CS402	Cloud Computing	3-1-0-4	CS204
4	CS403	Internet of Things	3-1-0-4	CS204
4	CS404	Computer Vision	3-1-0-4	CS302
4	CS405	Blockchain Technologies	3-1-0-4	CS303
5	CS501	Research Methodology	2-0-0-2	CS301
5	CS502	Special Topics in AI	3-1-0-4	CS302
5	CS503	Network Security	3-1-0-4	CS303
5	CS504	Data Visualization	3-1-0-4	CS304
5	CS505	Mobile Application Development	3-0-2-5	CS205
6	CS601	Capstone Project I	4-0-0-4	CS501
6	CS602	Capstone Project II	4-0-0-4	CS601
7	CS701	Internship	8-0-0-8	CS501
8	CS801	Final Year Thesis	8-0-0-8	CS602

Detailed Departmental Electives

Departmental electives in the Computer Science program at Ethics University Pauri Garhwal are designed to deepen students' understanding of specialized areas within the field. These courses allow students to tailor their education based on personal interests and career aspirations, ensuring they develop a competitive edge in the job market.

Machine Learning (CS302)

This course introduces students to fundamental concepts in machine learning, including supervised and unsupervised learning techniques. Students learn about decision trees, neural networks, clustering algorithms, regression models, and reinforcement learning. Through hands-on labs and projects, they gain practical experience in implementing ML models using Python and TensorFlow.

Cybersecurity Fundamentals (CS303)

This elective explores the principles of information security, covering topics such as encryption techniques, network security protocols, digital forensics, and risk management. Students study real-world case studies and participate in simulated attack scenarios to understand vulnerabilities and develop mitigation strategies.

Data Mining and Analytics (CS304)

This course focuses on extracting useful patterns and insights from large datasets using statistical methods and machine learning algorithms. Students learn data preprocessing, association rule mining, classification, clustering, and anomaly detection techniques. The course emphasizes practical applications in business intelligence and scientific research.

Human Computer Interaction (CS305)

This elective examines the design and evaluation of interactive systems from a user-centered perspective. Students explore usability principles, cognitive psychology, interface design patterns, and prototyping methodologies. They conduct user studies and evaluate interfaces using various tools and frameworks.

Advanced Algorithms (CS401)

This advanced course covers algorithmic design techniques such as dynamic programming, greedy algorithms, graph traversal methods, and complexity theory. Students analyze the efficiency of different algorithms and implement solutions for complex computational problems.

Cloud Computing (CS402)

This course explores cloud infrastructure models, virtualization technologies, and service delivery frameworks like IaaS, PaaS, and SaaS. Students gain hands-on experience with platforms such as AWS, Microsoft Azure, and Google Cloud Platform through lab exercises and project development.

Internet of Things (CS403)

This elective introduces students to the architecture, protocols, and applications of IoT systems. Topics include sensor networks, embedded systems programming, wireless communication, edge computing, and smart city implementations. Students work on projects involving real-time data collection and processing.

Computer Vision (CS404)

This course delves into image processing techniques, object detection, feature extraction, and deep learning applications in visual recognition tasks. Students learn to build computer vision systems using OpenCV and TensorFlow, focusing on practical implementation in robotics, medical imaging, and autonomous vehicles.

Blockchain Technologies (CS405)

This elective explores the underlying principles of blockchain, including distributed consensus mechanisms, smart contracts, and decentralized applications. Students study cryptocurrency systems, cryptographic hashing, and develop simple blockchain prototypes using Ethereum and Solidity.

Research Methodology (CS501)

This course teaches students how to conduct research in computer science, covering literature review techniques, hypothesis formulation, experimental design, and data analysis methods. It prepares them for writing academic papers, presenting findings at conferences, and pursuing graduate studies.

Special Topics in AI (CS502)

This advanced elective covers emerging trends in artificial intelligence such as natural language processing, computer vision, robotics, and ethical AI. Students explore cutting-edge research papers, participate in discussions, and engage in small-scale research projects.

Network Security (CS503)

This course examines advanced topics in network security including firewall design, intrusion detection systems, secure routing protocols, and vulnerability assessment techniques. Students analyze real-world attacks and implement defensive measures using industry-standard tools.

Data Visualization (CS504)

This elective focuses on creating meaningful visual representations of data to facilitate decision-making. Students learn visualization principles, use tools like Tableau and D3.js, and develop interactive dashboards for business intelligence and scientific analysis.

Mobile Application Development (CS505)

This course provides hands-on experience in building mobile applications for iOS and Android platforms. Students learn app design patterns, UI/UX principles, integration with APIs, and deployment processes. They work on projects involving real-world use cases such as fitness tracking, social networking, and e-commerce.

Project-Based Learning Philosophy

The department's philosophy on project-based learning is rooted in the belief that practical experience enhances theoretical knowledge and fosters innovation. Students engage in mini-projects throughout their academic journey, culminating in a final-year thesis or capstone project.

Mini-Projects: These are short-term projects spanning one to two semesters, typically involving group collaboration and real-world problem-solving. Mini-projects help students apply concepts learned in class to practical situations and build essential teamwork skills.

Final-Year Thesis/Capstone Project: The capstone project is a significant undertaking that allows students to showcase their expertise in a chosen area of interest. Students select topics aligned with their specialization, work closely with faculty mentors, and present their findings at the end of their program.

The selection process for projects involves identifying student interests, aligning with faculty research areas, and ensuring feasibility within the given timeframe. Faculty mentors guide students through each stage of development, from initial planning to final presentation, ensuring quality outcomes and meaningful learning experiences.