Comprehensive Course Structure Overview

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	CS101	Introduction to Programming with C	3-0-2-4	-
1	CS102	Mathematics for Computer Applications I	3-0-2-4	-
1	CS103	Physics for Computer Science	3-0-2-4	-
1	CS104	English for Technical Communication	2-0-2-3	-
1	CS105	Computer Organization and Architecture	3-0-2-4	-
1	CS106	Programming Lab with C	0-0-4-2	-
2	CS201	Data Structures and Algorithms	3-0-2-4	CS101
2	CS202	Mathematics for Computer Applications II	3-0-2-4	CS102
2	CS203	Object Oriented Programming with Java	3-0-2-4	CS101
2	CS204	Electronics and Communication Fundamentals	3-0-2-4	-
2	CS205	Database Management Systems	3-0-2-4	-
2	CS206	Java Programming Lab	0-0-4-2	CS101
3	CS301	Operating Systems	3-0-2-4	CS201, CS205
3	CS302	Computer Networks	3-0-2-4	CS201
3	CS303	Software Engineering	3-0-2-4	CS201, CS205
3	CS304	Discrete Mathematics	3-0-2-4	CS102
3	CS305	Web Technologies	3-0-2-4	CS203
3	CS306	Web Development Lab	0-0-4-2	CS203, CS205
4	CS401	Artificial Intelligence and Machine Learning	3-0-2-4	CS201, CS304
4	CS402	Cybersecurity Fundamentals	3-0-2-4	CS301, CS302
4	CS403	Cloud Computing	3-0-2-4	CS301, CS302
4	CS404	Data Science and Analytics	3-0-2-4	CS201, CS202
4	CS405	Mobile Application Development	3-0-2-4	CS203, CS305
4	CS406	Mobile App Development Lab	0-0-4-2	CS203, CS305
5	CS501	Advanced Algorithms	3-0-2-4	CS201
5	CS502	Human Computer Interaction	3-0-2-4	CS201, CS303
5	CS503	Internet of Things	3-0-2-4	CS301, CS302
5	CS504	Research Methodology	3-0-2-4	-
5	CS505	Project Management	3-0-2-4	-
5	CS506	Mini Project I	0-0-8-4	-
6	CS601	Advanced Machine Learning	3-0-2-4	CS401
6	CS602	Network Security	3-0-2-4	CS402
6	CS603	DevOps and Containerization	3-0-2-4	CS301, CS303
6	CS604	Big Data Analytics	3-0-2-4	CS404
6	CS605	Mobile Application Development II	3-0-2-4	CS405
6	CS606	Mini Project II	0-0-8-4	CS506
7	CS701	Capstone Project I	0-0-12-8	CS606
7	CS702	Advanced Topics in Computer Applications	3-0-2-4	-
7	CS703	Entrepreneurship in Tech	3-0-2-4	-
7	CS704	Internship Program	0-0-12-8	-
8	CS801	Capstone Project II	0-0-12-8	CS701
8	CS802	Industry Exposure Seminar	3-0-2-4	-
8	CS803	Research Paper Writing and Presentation	3-0-2-4	-

Detailed Departmental Elective Courses

The department offers a wide range of advanced elective courses designed to deepen students' understanding and provide specialized skills in emerging areas:

• Advanced Machine Learning: This course explores deep learning architectures, neural networks, and reinforcement learning techniques. Students will work on real-world datasets and build predictive models for various domains including healthcare, finance, and autonomous systems.
• Network Security: Focuses on protecting network infrastructures against cyber threats. Topics include firewalls, intrusion detection systems, secure protocols, and cryptography. Practical labs involve setting up virtual networks and conducting penetration testing exercises.
• DevOps and Containerization: Covers continuous integration/continuous deployment (CI/CD) pipelines using tools like Jenkins, Docker, and Kubernetes. Students learn to automate software delivery processes and manage cloud-based applications at scale.
• Big Data Analytics: Introduces students to big data processing frameworks such as Apache Hadoop and Spark. The course emphasizes data warehousing, real-time streaming analytics, and visualization techniques for handling large datasets efficiently.
• Mobile Application Development II: Builds upon the foundational knowledge gained in earlier semesters. Students develop advanced mobile apps with features like offline functionality, location services, and integration with backend APIs using modern frameworks.
• Internet of Things (IoT) Systems: Explores IoT architecture, sensor networks, embedded systems programming, and connectivity protocols. Students design and implement smart home or industrial automation solutions using Raspberry Pi, Arduino, and other platforms.
• Human-Computer Interaction: Emphasizes user-centered design principles and usability studies. Students conduct research on human behavior in digital environments, prototype interfaces, and evaluate user experiences through experiments and surveys.
• Cloud Computing Technologies: Provides an in-depth look at cloud service models (IaaS, PaaS, SaaS), virtualization technologies, and cloud security measures. Practical sessions involve deploying applications on AWS, Azure, and Google Cloud platforms.
• Data Science and Analytics: Combines statistics, programming, and domain expertise to extract insights from complex datasets. Students use Python, R, SQL, and machine learning libraries to perform exploratory data analysis, build predictive models, and communicate findings effectively.
• Artificial Intelligence in Robotics: Integrates AI concepts with robotics engineering. Students design intelligent robots that can perceive environments, make decisions, and interact with humans using sensors, actuators, and control systems.

Project-Based Learning Philosophy

The department's philosophy on project-based learning is centered around experiential education, where students learn by doing. Projects are structured to mirror real-world challenges and provide opportunities for innovation and creativity.

The mandatory mini-projects in the fifth semester allow students to apply theoretical knowledge to practical problems. These projects typically span 8 weeks and involve working in teams of 3-5 members under faculty supervision. Students must document their process, present findings, and receive feedback from both peers and mentors.

The final-year capstone project provides a comprehensive learning experience where students undertake an extended research or development endeavor. This project spans two semesters (7th and 8th) and requires significant independent work, including literature review, problem definition, methodology design, implementation, testing, and documentation.

Students select their projects based on their interests and career goals, often aligning with ongoing faculty research initiatives or industry partnerships. Faculty mentors guide students through each phase of the project, ensuring academic rigor while encouraging innovation and entrepreneurship.