Course Structure Across 8 Semesters

SEMESTER	COURSE CODE	COURSE TITLE	CREDIT STRUCTURE (L-T-P-C)	PRE-REQUISITES
1	CS101	Introduction to Programming	3-0-2-4	None
1	CS102	Discrete Mathematics	3-0-2-4	None
1	CS103	Data Structures and Algorithms	3-0-2-4	CS101
1	CS104	Computer Organization	3-0-2-4	None
1	CS105	Physics for Computer Science	3-0-2-4	None
1	CS106	English Communication Skills	3-0-2-4	None
2	CS201	Object-Oriented Programming with Java	3-0-2-4	CS101
2	CS202	Database Management Systems	3-0-2-4	CS103
2	CS203	Operating Systems	3-0-2-4	CS104
2	CS204	Computer Networks	3-0-2-4	CS104
2	CS205	Mathematics for Computer Science	3-0-2-4	CS102
2	CS206	Statistics and Probability	3-0-2-4	None
3	CS301	Software Engineering	3-0-2-4	CS201
3	CS302	Design and Analysis of Algorithms	3-0-2-4	CS103
3	CS303	Digital Logic and Microprocessor	3-0-2-4	CS104
3	CS304	Artificial Intelligence	3-0-2-4	CS202, CS205
3	CS305	Web Technologies	3-0-2-4	CS201
3	CS306	Computer Graphics and Visualization	3-0-2-4	CS201, CS103
4	CS401	Machine Learning	3-0-2-4	CS302, CS206
4	CS402	Cybersecurity Fundamentals	3-0-2-4	CS204
4	CS403	Big Data Analytics	3-0-2-4	CS202, CS206
4	CS404	Mobile Application Development	3-0-2-4	CS305
4	CS405	Human Computer Interaction	3-0-2-4	CS301
4	CS406	Embedded Systems	3-0-2-4	CS303
5	CS501	Advanced Algorithms	3-0-2-4	CS302
5	CS502	Cloud Computing	3-0-2-4	CS204
5	CS503	Quantum Computing	3-0-2-4	CS301, CS205
5	CS504	Blockchain Technologies	3-0-2-4	CS202
5	CS505	Computer Vision	3-0-2-4	CS401
5	CS506	Game Development	3-0-2-4	CS306
6	CS601	Deep Learning and Neural Networks	3-0-2-4	CS401
6	CS602	Security Auditing and Penetration Testing	3-0-2-4	CS402
6	CS603	Data Mining and Warehousing	3-0-2-4	CS403
6	CS604	Internet of Things (IoT)	3-0-2-4	CS304
6	CS605	Advanced Human Computer Interaction	3-0-2-4	CS505
6	CS606	Mobile and Wireless Networks	3-0-2-4	CS204
7	CS701	Research Methodology	3-0-2-4	None
7	CS702	Capstone Project I	0-0-6-8	CS601, CS602
7	CS703	Special Topics in Computer Science	3-0-2-4	CS501
7	CS704	Internship	0-0-0-12	CS603, CS604
8	CS801	Capstone Project II	0-0-6-8	CS702
8	CS802	Advanced Elective I	3-0-2-4	CS703
8	CS803	Advanced Elective II	3-0-2-4	CS802
8	CS804	Thesis Proposal	0-0-0-6	CS701

Detailed Course Descriptions for Departmental Electives

These advanced elective courses are designed to deepen students' understanding of specific areas within Computer Science and provide them with specialized skills relevant to their chosen career paths.

Deep Learning and Neural Networks

This course explores the fundamentals of neural networks, including feedforward, convolutional, recurrent, and transformer architectures. Students will learn how to implement models using frameworks like TensorFlow or PyTorch, analyze performance metrics, and apply techniques for regularization and optimization. The course also covers recent advancements in generative models, reinforcement learning, and unsupervised learning.

Security Auditing and Penetration Testing

This elective focuses on identifying vulnerabilities in network systems and applications through hands-on exercises and simulations. Students will learn to use tools like Nessus, Metasploit, and Burp Suite to conduct security assessments. The course includes ethical hacking practices, incident response protocols, and compliance frameworks such as ISO 27001.

Data Mining and Warehousing

This course introduces students to data mining techniques used in extracting patterns from large datasets. Topics include clustering, classification, association rule mining, anomaly detection, and visualization methods. Students will gain experience using tools like Weka, RapidMiner, and Python libraries such as Scikit-learn and Pandas.

Internet of Things (IoT)

The Internet of Things represents a paradigm shift in how devices interact with each other. This course covers IoT architecture, sensor technologies, communication protocols (e.g., MQTT, CoAP), cloud integration, and edge computing concepts. Students will build practical IoT projects using platforms like Arduino, Raspberry Pi, and AWS IoT Core.

Advanced Human Computer Interaction

This course delves into advanced topics in human-computer interaction design, including accessibility, virtual reality interfaces, mobile UX, and behavioral psychology. Students will conduct user research studies, prototype interfaces, and evaluate usability through various methodologies. The course emphasizes ethical considerations and inclusive design principles.

Mobile and Wireless Networks

This elective provides an in-depth understanding of wireless communication technologies and mobile network architectures. Students will explore cellular networks (2G, 3G, 4G, 5G), Wi-Fi standards, Bluetooth protocols, and satellite communications. Practical labs involve simulating wireless environments using tools like ns-3 and analyzing network performance metrics.

Research Methodology

This course prepares students for conducting independent research by introducing them to scientific methods, hypothesis formation, data collection techniques, and academic writing. It emphasizes the importance of reproducibility, ethical considerations in research, and literature review processes. Students will develop a research proposal and present it to faculty members.

Capstone Project I

The first phase of the capstone project involves defining the scope, setting objectives, and selecting appropriate methodologies for research or development tasks. Students work under faculty supervision to design and plan their projects, ensuring alignment with current industry trends and academic rigor. This stage includes literature surveys, feasibility analysis, and preliminary prototyping.

Special Topics in Computer Science

This course allows students to explore emerging areas in computer science that are not covered in standard curricula. Examples include quantum computing, blockchain applications, ethical AI, and bioinformatics. The content is updated annually based on faculty expertise and industry relevance.

Internship

The internship component provides students with real-world experience working in a professional environment. Students collaborate with industry partners to apply theoretical knowledge to practical problems. Internships typically last 6-12 months and offer mentorship, project guidance, and potential full-time employment opportunities.

Capstone Project II

The final phase of the capstone project involves completing the research or development work begun in Capstone I. Students deliver a comprehensive report, demonstrate their findings to faculty and peers, and receive feedback for future improvements. This stage also includes preparing presentations for potential conferences or publications.

Advanced Elective I & II

These elective courses allow students to specialize further in areas such as data science, cybersecurity, software engineering, or artificial intelligence. Each course is tailored to meet specific learning outcomes and aligns with the student's chosen track within the program.

Thesis Proposal

This stage requires students to submit a detailed proposal outlining their thesis research topic, methodology, timeline, and expected outcomes. The proposal is reviewed by a committee of faculty members who provide guidance on refining the scope and ensuring feasibility.

Project-Based Learning Philosophy

At F S University Firozabad, we believe that true mastery comes from applying knowledge in meaningful contexts. Our project-based learning approach integrates theory with practice, encouraging students to engage deeply with real-world challenges while building essential technical and soft skills.

Mini Projects (Semesters 1-6)

Throughout their undergraduate journey, students undertake a series of mini projects that build upon each other. These projects are designed to reinforce concepts learned in class and develop problem-solving abilities. Each project is assigned by faculty mentors who guide students through the process of defining requirements, designing solutions, implementing code, testing functionality, and documenting outcomes.

Final-Year Thesis/Capstone Project

The capstone project represents the culmination of a student's academic journey in Computer Science. Students select topics that align with their interests and career goals, often inspired by industry needs or emerging research areas. The process involves extensive literature review, experimental design, data collection, analysis, and presentation of findings.

Project Selection Process

Students begin exploring project ideas during the third semester. They attend workshops, review faculty research interests, and engage in discussions with mentors. The final selection is made after consultation with advisors, ensuring that projects are both challenging and achievable within the given timeframe.

Evaluation Criteria

Projects are evaluated based on multiple criteria including technical execution, innovation, documentation quality, presentation skills, and teamwork effectiveness. Faculty members and external reviewers assess each project to ensure it meets academic standards and demonstrates practical relevance.