Course Structure
The Computer Science and Engineering program at Chinmaya Vishwavidyapeeth is structured over 8 semesters, with each semester comprising a blend of core courses, departmental electives, science electives, and laboratory sessions. The curriculum emphasizes both theoretical knowledge and practical application to prepare students for diverse career paths in the technology industry.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics I | 4-0-0-4 | - |
| 1 | CS103 | Physics I | 3-0-0-3 | - |
| 1 | CS104 | Chemistry I | 3-0-0-3 | - |
| 1 | CS105 | Engineering Graphics | 2-0-0-2 | - |
| 1 | CS106 | Workshop Practice | 0-0-3-1 | - |
| 2 | CS201 | Data Structures and Algorithms | 4-0-0-4 | CS101 |
| 2 | CS202 | Mathematics II | 4-0-0-4 | CS102 |
| 2 | CS203 | Physics II | 3-0-0-3 | CS103 |
| 2 | CS204 | Electrical Circuits and Networks | 3-0-0-3 | - |
| 2 | CS205 | Computer Organization | 3-0-0-3 | - |
| 2 | CS206 | Laboratory Session | 0-0-3-1 | CS101 |
| 3 | CS301 | Operating Systems | 4-0-0-4 | CS201, CS205 |
| 3 | CS302 | Database Management Systems | 4-0-0-4 | CS201 |
| 3 | CS303 | Computer Networks | 4-0-0-4 | CS204 |
| 3 | CS304 | Mathematics III | 4-0-0-4 | CS202 |
| 3 | CS305 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 3 | CS306 | Laboratory Session | 0-0-3-1 | CS205 |
| 4 | CS401 | Software Engineering | 4-0-0-4 | CS301, CS302 |
| 4 | CS402 | Compiler Design | 4-0-0-4 | CS301, CS305 |
| 4 | CS403 | Artificial Intelligence | 4-0-0-4 | CS301, CS304 |
| 4 | CS404 | Web Technologies | 4-0-0-4 | CS305 |
| 4 | CS405 | Mathematics IV | 4-0-0-4 | CS304 |
| 4 | CS406 | Laboratory Session | 0-0-3-1 | CS302, CS305 |
| 5 | CS501 | Cybersecurity Fundamentals | 4-0-0-4 | CS301, CS303 |
| 5 | CS502 | Data Science | 4-0-0-4 | CS302, CS304 |
| 5 | CS503 | Human-Computer Interaction | 4-0-0-4 | CS301 |
| 5 | CS504 | Embedded Systems | 4-0-0-4 | CS205 |
| 5 | CS505 | Mobile Application Development | 4-0-0-4 | CS305 |
| 5 | CS506 | Laboratory Session | 0-0-3-1 | CS401, CS402 |
| 6 | CS601 | Cloud Computing | 4-0-0-4 | CS303, CS501 |
| 6 | CS602 | Big Data Analytics | 4-0-0-4 | CS502 |
| 6 | CS603 | Computer Vision | 4-0-0-4 | CS301, CS502 |
| 6 | CS604 | Machine Learning | 4-0-0-4 | CS304, CS502 |
| 6 | CS605 | Internet of Things | 4-0-0-4 | CS504 |
| 6 | CS606 | Laboratory Session | 0-0-3-1 | CS502, CS503 |
| 7 | CS701 | Advanced Algorithms | 4-0-0-4 | CS201 |
| 7 | CS702 | Research Methodology | 3-0-0-3 | - |
| 7 | CS703 | Capstone Project I | 4-0-0-4 | - |
| 7 | CS704 | Internship Preparation | 2-0-0-2 | - |
| 7 | CS705 | Elective Course I | 3-0-0-3 | - |
| 7 | CS706 | Laboratory Session | 0-0-3-1 | - |
| 8 | CS801 | Capstone Project II | 4-0-0-4 | CS703 |
| 8 | CS802 | Professional Practices | 2-0-0-2 | - |
| 8 | CS803 | Elective Course II | 3-0-0-3 | - |
| 8 | CS804 | Thesis Writing | 2-0-0-2 | - |
| 8 | CS805 | Final Project Defense | 0-0-0-3 | CS703, CS801 |
| 8 | CS806 | Laboratory Session | 0-0-3-1 | - |
Advanced Departmental Electives
The department offers several advanced elective courses that allow students to delve deeper into specialized areas of interest. These courses are designed to provide in-depth knowledge and practical skills required for industry readiness and research endeavors.
Machine Learning: This course explores the fundamentals of machine learning algorithms, including supervised and unsupervised learning techniques. Students will learn how to implement various ML models using Python and TensorFlow, with a focus on real-world applications in data science, computer vision, and natural language processing.
Computer Vision: Focused on image processing and pattern recognition, this course covers topics such as edge detection, feature extraction, object recognition, and deep learning architectures for visual tasks. Students will gain hands-on experience with OpenCV and other computer vision libraries.
Cybersecurity Fundamentals: This course introduces students to the principles of cybersecurity, covering network security protocols, cryptography, ethical hacking, and risk management strategies. It prepares students for roles in security analysis, penetration testing, and compliance auditing.
Data Science: Designed to equip students with tools and techniques for data analysis, this course covers statistical methods, data visualization, machine learning algorithms, and big data platforms such as Apache Spark and Hadoop. Students will work on real-world datasets to build predictive models and derive actionable insights.
Big Data Analytics: This advanced elective focuses on handling large-scale datasets using distributed computing frameworks. Topics include MapReduce, HDFS, YARN, and NoSQL databases. Students will gain experience with Apache Spark, Hive, and Pig for processing big data efficiently.
Cloud Computing: Students will explore cloud service models (IaaS, PaaS, SaaS), virtualization technologies, and cloud deployment strategies. The course includes hands-on labs using AWS, Azure, and Google Cloud Platform to deploy scalable applications and services.
Embedded Systems: This course provides a comprehensive understanding of embedded systems design and development, including microcontroller programming, real-time operating systems, and hardware-software co-design. Students will work on projects involving IoT devices and smart sensors.
Internet of Things (IoT): Covering the architecture and implementation of IoT solutions, this course discusses sensor networks, communication protocols, cloud integration, and security considerations. Practical sessions involve building IoT prototypes using Raspberry Pi and Arduino platforms.
Mobile Application Development: Focused on developing mobile applications for Android and iOS platforms, this course covers UI/UX design, app lifecycle management, API integration, and backend services. Students will create fully functional apps using Kotlin, Swift, React Native, and Flutter frameworks.
Human-Computer Interaction: This course emphasizes the principles of designing user-friendly interfaces and evaluating usability. Topics include interaction design models, prototyping techniques, accessibility standards, and cognitive psychology in interface design.
Software Engineering: Designed to teach systematic approaches to software development, this course covers software lifecycle models, requirement analysis, testing strategies, and project management methodologies. Students will gain experience with agile frameworks like Scrum and Kanban.
Compiler Design: Focused on the construction of compilers and interpreters, this course explores lexical analysis, parsing techniques, semantic analysis, code generation, and optimization strategies. Students will build a simple compiler for a subset of C language.
Web Technologies: This elective covers modern web development practices, including HTML5, CSS3, JavaScript frameworks like React and Angular, RESTful APIs, and database integration. Students will develop full-stack applications using Node.js and MongoDB.
Database Management Systems: Covering relational and non-relational database design, this course discusses normalization, transaction management, indexing strategies, and query optimization. Students will work with MySQL, PostgreSQL, and MongoDB to manage complex datasets.
Operating Systems: This course provides a deep dive into operating system concepts, including process scheduling, memory management, file systems, and security mechanisms. Students will gain hands-on experience through lab sessions involving Linux kernel modules and system-level programming.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a cornerstone of technical education. The approach emphasizes real-world problem-solving, teamwork, and innovation. Students are encouraged to apply theoretical knowledge to practical scenarios through structured mini-projects and capstone projects.
Mini-projects are assigned during the third and fourth years, allowing students to explore specific domains and develop hands-on skills. These projects typically last for one semester and are evaluated based on technical execution, creativity, presentation quality, and peer feedback.
The final-year thesis/capstone project is a significant milestone in the academic journey. Students select a research topic under faculty mentorship and work independently or in small teams to develop a comprehensive solution. The project involves literature review, methodology development, implementation, testing, documentation, and presentation.
Students can choose projects related to their area of specialization or propose innovative ideas that align with current industry trends. Faculty mentors guide students throughout the process, providing technical support, feedback, and career guidance.