Course Structure Overview
The curriculum for the Computer Science program at Institute Of Advanced Research Gandhinagar is meticulously structured to ensure a balanced blend of theoretical knowledge and practical application. The program spans four years, divided into eight semesters, with each semester containing a mix of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
| 1 | CS101 | Introduction to Computing | 3-0-0-3 | - |
| 1 | MA101 | Calculus I | 4-0-0-4 | - |
| 1 | PH101 | Physics for Computer Science | 3-0-0-3 | - |
| 1 | CS102 | Programming in C | 2-0-2-3 | - |
| 1 | HS101 | English Communication Skills | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS102 |
| 2 | MA201 | Calculus II | 4-0-0-4 | MA101 |
| 2 | PH201 | Electronics for Computing | 3-0-0-3 | PH101 |
| 2 | CS202 | Object-Oriented Programming in Java | 2-0-2-3 | CS102 |
| 2 | ES201 | Engineering Drawing | 2-0-0-2 | - |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | MA301 | Probability and Statistics | 3-0-0-3 | MA201 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS201 |
| 3 | CS303 | Computer Architecture | 3-0-0-3 | PH201 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS201 |
| 4 | CS401 | Computer Networks | 3-0-0-3 | CS301 |
| 4 | CS402 | Compiler Design | 3-0-0-3 | CS301 |
| 4 | CS403 | Human Computer Interaction | 3-0-0-3 | CS201 |
| 4 | CS404 | Mobile Application Development | 2-0-2-3 | CS202 |
| 4 | MA401 | Discrete Mathematics | 3-0-0-3 | MA201 |
| 5 | CS501 | Machine Learning Fundamentals | 3-0-0-3 | CS401 |
| 5 | CS502 | Cybersecurity Principles | 3-0-0-3 | CS401 |
| 5 | CS503 | Data Mining and Warehousing | 3-0-0-3 | CS301 |
| 5 | CS504 | Advanced Software Engineering | 3-0-0-3 | CS404 |
| 5 | CS505 | Embedded Systems Design | 3-0-0-3 | CS303 |
| 6 | CS601 | Deep Learning with TensorFlow | 3-0-0-3 | CS501 |
| 6 | CS602 | Network Security | 3-0-0-3 | CS502 |
| 6 | CS603 | Big Data Analytics | 3-0-0-3 | CS503 |
| 6 | CS604 | Cloud Computing | 3-0-0-3 | CS401 |
| 6 | CS605 | Internet of Things (IoT) | 3-0-0-3 | CS505 |
| 7 | CS701 | Advanced Computer Vision | 3-0-0-3 | CS601 |
| 7 | CS702 | Quantum Algorithms | 3-0-0-3 | CS501 |
| 7 | CS703 | Digital Forensics | 3-0-0-3 | CS502 |
| 7 | CS704 | Software Architecture and Design Patterns | 3-0-0-3 | CS604 |
| 7 | CS705 | Research Project I | 2-0-2-3 | - |
| 8 | CS801 | Final Year Thesis/Capstone Project | 4-0-0-4 | CS705 |
| 8 | CS802 | Industry Internship | 2-0-0-2 | - |
| 8 | CS803 | Advanced Electives | 2-0-0-2 | - |
Advanced Departmental Elective Courses
Deep Learning with TensorFlow: This course introduces students to the principles and practices of deep learning using TensorFlow. Topics include neural networks, convolutional networks, recurrent networks, reinforcement learning, and practical applications in image recognition, natural language processing, and computer vision.
Network Security: Focused on protecting data and systems from unauthorized access, this course covers cryptographic protocols, firewalls, intrusion detection systems, and secure network design. Students engage in hands-on labs to simulate real-world security scenarios.
Big Data Analytics: This elective explores techniques for processing and analyzing large datasets using tools like Apache Spark, Hadoop, and Python libraries. It covers data mining algorithms, statistical modeling, and visualization methods.
Cloud Computing: Students learn about cloud infrastructure, virtualization technologies, distributed systems, and service models such as IaaS, PaaS, and SaaS. The course includes projects involving deployment on platforms like AWS and Azure.
Internet of Things (IoT): This course covers IoT architectures, sensor networks, embedded systems, wireless communication protocols, and application development for smart environments. Students build prototypes using Raspberry Pi and Arduino.
Advanced Computer Vision: This advanced topic delves into computer vision algorithms including object detection, segmentation, tracking, and 3D reconstruction. Students work with datasets from competitions like ImageNet and COCO.
Quantum Algorithms: Introducing quantum computing concepts, this course covers qubits, superposition, entanglement, quantum gates, and algorithms such as Shor’s algorithm and Grover's search algorithm.
Digital Forensics: This course explores digital evidence collection, preservation, analysis, and reporting. Students learn forensic tools and techniques used in investigations involving cybercrime and data breaches.
Software Architecture and Design Patterns: Focused on scalable software design, this course covers architectural styles, design patterns, microservices, and enterprise integration frameworks.
Research Project I: A foundational research project where students explore a topic under faculty supervision, culminating in a literature review and initial experimental setup.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around fostering innovation, collaboration, and real-world problem-solving skills. Projects are assigned at different stages of the program to ensure gradual skill development.
Mini-projects are introduced in the third semester, allowing students to apply theoretical concepts learned in core courses. These projects typically last 4-6 weeks and involve small teams working under faculty guidance. Evaluation criteria include technical execution, presentation quality, teamwork, and adherence to deadlines.
The final-year thesis or capstone project is a comprehensive endeavor that spans the entire eighth semester. Students select topics aligned with their specialization interests or industry requirements. They work closely with faculty mentors who provide academic support, resource access, and feedback throughout the process.
Project selection involves a proposal phase where students present ideas to the departmental advisory board. Criteria for selection include feasibility, relevance to current trends, novelty, and alignment with research goals. Faculty members evaluate proposals based on originality, technical depth, and potential impact.