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.