Comprehensive Course Structure

Semester	Course Code	Course Title	Credits (L-T-P-C)	Prerequisites
I	CS101	Introduction to Programming	3-0-0-3	-
I	CS102	Mathematics for Computer Science	4-0-0-4	-
I	CS103	Engineering Graphics and Design	2-0-0-2	-
I	CS104	Physics for Computer Science	3-0-0-3	-
I	CS105	Chemistry for Computer Science	3-0-0-3	-
I	CS106	English for Technical Communication	2-0-0-2	-
I	CS107	Introduction to Data Structures	3-0-0-3	CS101
I	CS108	Basics of Computer Organization	3-0-0-3	-
I	CS109	Programming Lab	0-0-4-2	CS101
I	CS110	Data Structures Lab	0-0-4-2	CS107
II	CS201	Discrete Mathematics	3-0-0-3	CS102
II	CS202	Algorithms and Complexity	3-0-0-3	CS107
II	CS203	Database Management Systems	3-0-0-3	CS107
II	CS204	Object-Oriented Programming	3-0-0-3	CS101
II	CS205	Computer Networks	3-0-0-3	CS108
II	CS206	Operating Systems	3-0-0-3	CS108
II	CS207	Computer Architecture	3-0-0-3	CS108
II	CS208	Software Engineering	3-0-0-3	CS104
II	CS209	Object-Oriented Programming Lab	0-0-4-2	CS204
II	CS210	Database Lab	0-0-4-2	CS203
III	CS301	Design and Analysis of Algorithms	3-0-0-3	CS202
III	CS302	Artificial Intelligence and Machine Learning	3-0-0-3	CS202
III	CS303	Cybersecurity Fundamentals	3-0-0-3	CS205
III	CS304	Data Mining and Warehousing	3-0-0-3	CS203
III	CS305	Web Technologies and Applications	3-0-0-3	CS204
III	CS306	Mobile Computing	3-0-0-3	CS205
III	CS307	Human Computer Interaction	3-0-0-3	CS208
III	CS308	Database Systems Lab	0-0-4-2	CS304
III	CS309	AI and ML Lab	0-0-4-2	CS302
IV	CS401	Advanced Software Engineering	3-0-0-3	CS208
IV	CS402	Distributed Systems	3-0-0-3	CS205
IV	CS403	Cloud Computing	3-0-0-3	CS206
IV	CS404	Computer Vision and Image Processing	3-0-0-3	CS302
IV	CS405	Internet of Things (IoT)	3-0-0-3	CS206
IV	CS406	Game Development	3-0-0-3	CS205
IV	CS407	Blockchain Technology	3-0-0-3	CS205
IV	CS408	Quantitative Finance and Algorithmic Trading	3-0-0-3	CS304
IV	CS409	Distributed Systems Lab	0-0-4-2	CS402
IV	CS410	Capstone Project Lab	0-0-6-3	All previous courses

Advanced Departmental Electives

Advanced departmental electives are designed to provide specialized knowledge and practical skills in emerging areas of computer science. These courses allow students to tailor their education according to personal interests and career goals.

Artificial Intelligence and Machine Learning

This course explores the fundamentals of AI and ML, covering supervised learning, unsupervised learning, neural networks, deep learning architectures, natural language processing, computer vision, reinforcement learning, and ethical considerations in AI development. Students will implement real-world applications using Python frameworks like TensorFlow and PyTorch.

Cybersecurity Fundamentals

Students learn about network security threats, cryptographic protocols, penetration testing, incident response, digital forensics, and risk management strategies. The course emphasizes hands-on labs with tools like Wireshark, Metasploit, and Kali Linux for practical experience.

Data Mining and Warehousing

This course introduces techniques for extracting patterns from large datasets, including clustering, classification, association rule mining, anomaly detection, and data warehousing concepts. Students gain proficiency in SQL, Python, and machine learning libraries for data analysis.

Web Technologies and Applications

The curriculum covers modern web development frameworks like React, Angular, Node.js, and Express, along with database integration, RESTful APIs, authentication mechanisms, and responsive design principles. Students build full-stack applications during lab sessions.

Mobile Computing

Students explore mobile platform development using Android Studio and iOS Swift frameworks. Topics include mobile app architecture, user interface design, location-based services, cloud integration, and cross-platform development using React Native or Flutter.

Human Computer Interaction

This course focuses on designing interfaces that enhance usability and accessibility. Students learn about user research methods, prototyping techniques, usability testing, interaction design principles, and emerging technologies like VR/AR interfaces for immersive experiences.

Computer Vision and Image Processing

Students study image processing algorithms, object detection, facial recognition, segmentation techniques, and convolutional neural networks. Practical labs involve using OpenCV and deep learning frameworks to build computer vision systems.

Internet of Things (IoT)

This course delves into IoT architecture, sensor technologies, embedded programming, wireless communication protocols, cloud integration, edge computing, and smart city applications. Students work with Raspberry Pi and Arduino platforms in lab settings.

Game Development

Students learn game design principles, 3D modeling, animation techniques, physics simulation, and engine architecture using Unity or Unreal Engine. Labs focus on creating interactive experiences across multiple platforms.

Blockchain Technology

This course covers blockchain fundamentals, smart contracts, consensus mechanisms, cryptocurrency systems, decentralized applications (dApps), and enterprise blockchain implementations. Students implement blockchain solutions using Ethereum and Hyperledger frameworks.

Quantitative Finance and Algorithmic Trading

Students study financial modeling, portfolio optimization, derivatives pricing, quantitative trading strategies, risk management, and algorithmic execution. The course integrates Python libraries for financial data analysis and backtesting trading algorithms.

Project-Based Learning Philosophy

The department's philosophy on project-based learning is rooted in the belief that active engagement with real-world problems enhances learning outcomes. Projects are structured to mirror industry challenges, encouraging creativity, teamwork, and innovation.

Mini-projects begin in the second year, allowing students to apply theoretical concepts in practical scenarios. These projects involve small teams working under faculty supervision to develop prototypes or solve specific technical issues.

The final-year capstone project is a comprehensive endeavor that integrates all learned knowledge. Students select topics aligned with their specializations and collaborate closely with faculty mentors throughout the process.

Evaluation criteria include technical execution, creativity, presentation quality, documentation standards, and demonstration of problem-solving capabilities. Projects are assessed through peer reviews, mentor evaluations, and public presentations.

Faculty mentors are assigned based on student interests and project scope. Mentors provide guidance on methodology, timeline management, and resource allocation to ensure successful completion of projects.