Curriculum Overview

The Computer Science curriculum at G D Goenka University Gurugram is designed to provide a comprehensive foundation in theoretical and practical aspects of computing, followed by specialization opportunities in advanced domains. The program spans eight semesters, with each semester carrying specific credit distributions across lectures (L), tutorials (T), practicals (P), and credits (C).

Semester-wise Course Breakdown

Year/Semester	Course Code	Course Title	Credit (L-T-P-C)	Prerequisites
I Year / I Semester	CS101	Programming in C	3-0-2-4	-
I Year / I Semester	CS102	Computer Organization	3-0-2-4	-
I Year / I Semester	CS103	Mathematics for Computer Science	3-0-2-4	-
I Year / I Semester	CS104	Physics for Computing	3-0-2-4	-
I Year / I Semester	CS105	Introduction to Computing	3-0-2-4	-
I Year / II Semester	CS201	Data Structures and Algorithms	3-0-2-4	CS101
I Year / II Semester	CS202	Object-Oriented Programming in Java	3-0-2-4	CS101
I Year / II Semester	CS203	Digital Logic and Design	3-0-2-4	CS102
I Year / II Semester	CS204	Mathematics for Computing	3-0-2-4	CS103
I Year / II Semester	CS205	Discrete Mathematics	3-0-2-4	CS103
II Year / I Semester	CS301	Database Management Systems	3-0-2-4	CS201, CS202
II Year / I Semester	CS302	Operating Systems	3-0-2-4	CS201, CS203
II Year / I Semester	CS303	Computer Networks	3-0-2-4	CS203
II Year / I Semester	CS304	Software Engineering	3-0-2-4	CS202
II Year / I Semester	CS305	Probability and Statistics	3-0-2-4	CS103
II Year / II Semester	CS401	Compiler Design	3-0-2-4	CS301, CS302
II Year / II Semester	CS402	Web Technologies	3-0-2-4	CS202, CS301
II Year / II Semester	CS403	Distributed Systems	3-0-2-4	CS303, CS302
II Year / II Semester	CS404	Design and Analysis of Algorithms	3-0-2-4	CS201
II Year / II Semester	CS405	Linear Algebra and Numerical Methods	3-0-2-4	CS103
III Year / I Semester	CS501	Machine Learning	3-0-2-4	CS301, CS404
III Year / I Semester	CS502	Cybersecurity Fundamentals	3-0-2-4	CS303
III Year / I Semester	CS503	Data Mining and Analytics	3-0-2-4	CS301, CS305
III Year / I Semester	CS504	Embedded Systems	3-0-2-4	CS203
III Year / I Semester	CS505	Cloud Computing	3-0-2-4	CS302, CS303
III Year / II Semester	CS601	Advanced Computer Architecture	3-0-2-4	CS203
III Year / II Semester	CS602	Artificial Intelligence	3-0-2-4	CS501
III Year / II Semester	CS603	Computer Vision and Image Processing	3-0-2-4	CS501, CS503
III Year / II Semester	CS604	Natural Language Processing	3-0-2-4	CS501, CS503
III Year / II Semester	CS605	Internet of Things (IoT)	3-0-2-4	CS504
IV Year / I Semester	CS701	Capstone Project	3-0-6-9	All prior courses
IV Year / I Semester	CS702	Research Methodology	3-0-2-4	CS501, CS602
IV Year / I Semester	CS703	Special Topics in Computer Science	3-0-2-4	CS602
IV Year / II Semester	CS801	Internship	0-0-12-15	All prior courses
IV Year / II Semester	CS802	Final Year Thesis	3-0-6-9	All prior courses

Advanced Departmental Elective Courses

• Machine Learning (CS501): This course introduces students to fundamental algorithms and techniques in machine learning, including supervised and unsupervised learning methods. Students learn how to implement models using libraries like scikit-learn and TensorFlow. The course emphasizes real-world applications such as image classification, natural language processing, and recommendation systems.
• Cybersecurity Fundamentals (CS502): Designed for beginners in cybersecurity, this course covers essential topics such as network security, cryptography, ethical hacking, and incident response. Students gain hands-on experience with tools like Wireshark, Nmap, and Metasploit, preparing them for entry-level roles in information security.
• Data Mining and Analytics (CS503): This course explores data analysis techniques, including clustering, classification, regression, and association rule mining. Students work with real datasets using Python and SQL to extract meaningful insights and generate reports that inform business decisions.
• Embedded Systems (CS504): Focused on building systems that interact with physical environments, this course covers microcontroller programming, sensor integration, and real-time operating systems. Students design and prototype embedded applications for smart devices and automation projects.
• Cloud Computing (CS505): This course teaches cloud platforms such as AWS, Azure, and Google Cloud. Topics include virtualization, containerization, serverless computing, and infrastructure as code. Students gain experience deploying scalable applications using DevOps practices.
• Advanced Computer Architecture (CS601): Students explore modern processor designs, memory hierarchies, parallel processing, and cache optimization techniques. The course includes laboratory sessions where students simulate architectures using tools like gem5 and analyze performance metrics.
• Artificial Intelligence (CS602): This comprehensive course delves into AI concepts such as neural networks, reinforcement learning, game theory, and expert systems. Students implement AI models from scratch and experiment with deep learning frameworks to solve complex problems in robotics, image recognition, and autonomous vehicles.
• Computer Vision and Image Processing (CS603): Covering image enhancement, feature extraction, object detection, and segmentation, this course prepares students for careers in computer vision and multimedia applications. Practical sessions involve using OpenCV libraries to develop visual computing solutions.
• Natural Language Processing (CS604): This course focuses on understanding and generating human language through computational methods. Students study text preprocessing, sentiment analysis, named entity recognition, and machine translation, applying these techniques in chatbots and automated content generation systems.
• Internet of Things (IoT) (CS605): Designed to equip students with skills needed for IoT development, this course covers sensor networks, wireless communication protocols, edge computing, and data analytics for connected devices. Students build IoT applications using platforms like Arduino and Raspberry Pi.

Project-Based Learning Philosophy

The department places a strong emphasis on project-based learning as an integral part of the educational experience. Projects are designed to simulate real-world scenarios, encouraging students to apply theoretical knowledge in practical settings while developing problem-solving and teamwork skills.

Mini-projects begin in the second year, where students work individually or in small groups on specific tasks such as developing a simple web application, designing an algorithm for sorting data, or creating a basic mobile app. These projects are evaluated based on design documentation, functionality, presentation, and peer feedback.

The final-year capstone project is a significant milestone that requires students to propose, plan, execute, and present a substantial research or development initiative. Students select topics aligned with their interests and career goals, often collaborating with faculty members or industry partners. The project involves extensive literature review, experimentation, documentation, and oral defense.

Faculty mentors play a crucial role in guiding students through each phase of the project process. Regular meetings are scheduled to discuss progress, troubleshoot issues, and ensure alignment with academic standards. Evaluation criteria include innovation, technical proficiency, presentation quality, and overall impact.

The department also hosts annual project showcases where students present their work to faculty, industry experts, and fellow students. This platform encourages peer learning, networking, and recognition of outstanding contributions to the field of computer science.