Course Structure Overview
The Computer Science program at Mahatma Gandhi University Of Medical Sciences And Technology Jaipur is structured over eight semesters, with a balanced mix of core courses, departmental electives, science electives, and laboratory sessions. Each semester carries a credit structure that ensures comprehensive coverage of theoretical and practical aspects.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming using C++ | 3-0-2-4 | - |
| I | CS102 | Mathematics for Computing | 3-0-0-3 | - |
| I | CS103 | Physics for Engineers | 3-0-0-3 | - |
| I | CS104 | Logic Design | 3-0-0-3 | - |
| I | CS105 | Introduction to Computing | 2-0-0-2 | - |
| I | CS106 | English for Technical Communication | 2-0-0-2 | - |
| II | CS201 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| II | CS202 | Discrete Mathematics | 3-0-0-3 | - |
| II | CS203 | Database Management Systems | 3-0-2-4 | CS101 |
| II | CS204 | Computer Organization and Architecture | 3-0-0-3 | CS104 |
| II | CS205 | Object Oriented Programming using Java | 3-0-2-4 | CS101 |
| II | CS206 | Introduction to Software Engineering | 2-0-0-2 | - |
| III | CS301 | Operating Systems | 3-0-2-4 | CS204, CS205 |
| III | CS302 | Computer Networks | 3-0-2-4 | CS204 |
| III | CS303 | Software Design and Architecture | 3-0-2-4 | CS205 |
| III | CS304 | Web Technologies | 3-0-2-4 | CS205 |
| III | CS305 | Statistics and Probability | 3-0-0-3 | CS202 |
| III | CS306 | Computer Graphics | 3-0-2-4 | CS205 |
| IV | CS401 | Artificial Intelligence | 3-0-2-4 | CS201, CS305 |
| IV | CS402 | Machine Learning | 3-0-2-4 | CS305 |
| IV | CS403 | Cybersecurity Fundamentals | 3-0-2-4 | CS204, CS302 |
| IV | CS404 | Data Mining and Warehousing | 3-0-2-4 | CS305 |
| IV | CS405 | Human Computer Interaction | 3-0-2-4 | - |
| IV | CS406 | Mobile Computing | 3-0-2-4 | CS304 |
| V | CS501 | Big Data Analytics | 3-0-2-4 | CS404 |
| V | CS502 | Distributed Systems | 3-0-2-4 | CS302 |
| V | CS503 | Quantum Computing | 3-0-2-4 | - |
| V | CS504 | Embedded Systems | 3-0-2-4 | CS204 |
| V | CS505 | Database Security | 3-0-2-4 | CS203 |
| V | CS506 | Advanced Web Development | 3-0-2-4 | CS304 |
| VI | CS601 | Deep Learning | 3-0-2-4 | CS402 |
| VI | CS602 | Information Retrieval | 3-0-2-4 | CS404 |
| VI | CS603 | Cloud Computing | 3-0-2-4 | CS302 |
| VI | CS604 | Compiler Design | 3-0-2-4 | CS201 |
| VI | CS605 | Network Security | 3-0-2-4 | CS302 |
| VI | CS606 | Image Processing | 3-0-2-4 | CS306 |
| VII | CS701 | Research Methodology | 2-0-0-2 | - |
| VII | CS702 | Capstone Project I | 3-0-6-9 | CS501, CS502, CS601 |
| VIII | CS801 | Capstone Project II | 3-0-6-9 | CS702 |
| VIII | CS802 | Internship | 0-0-0-15 | - |
Detailed Elective Course Descriptions
Artificial Intelligence: This course explores the principles and techniques of artificial intelligence, including knowledge representation, reasoning, planning, learning, and perception. Students will learn to implement AI algorithms using Python and apply them to real-world problems in robotics, computer vision, and natural language processing.
Machine Learning: Focused on statistical methods for pattern recognition and data analysis, this course covers supervised and unsupervised learning, neural networks, decision trees, support vector machines, and clustering algorithms. Students will gain hands-on experience with popular ML frameworks like TensorFlow and Scikit-learn.
Cybersecurity Fundamentals: This course introduces students to the core concepts of cybersecurity, including cryptography, network security, system security, and ethical hacking. It covers vulnerability assessment, penetration testing, and incident response strategies essential for protecting digital assets.
Data Mining and Warehousing: Students will learn how to extract meaningful patterns from large datasets using data mining techniques such as association rule mining, classification, clustering, and anomaly detection. The course also includes practical applications in data warehousing and business intelligence.
Human Computer Interaction: This elective focuses on designing user-friendly interfaces and evaluating usability in software applications. Topics include cognitive psychology, user experience design, prototyping, and accessibility standards to create inclusive digital products.
Mobile Computing: The course covers mobile application development using platforms like Android and iOS. Students will explore mobile architectures, APIs, location-based services, and mobile security challenges in the context of emerging technologies such as wearables and IoT.
Big Data Analytics: This course introduces students to big data processing using Hadoop, Spark, and NoSQL databases. It covers data streaming, distributed computing, and analytics pipelines for handling massive datasets in real-time environments.
Distributed Systems: Students will study the design and implementation of systems that span multiple computers, focusing on concepts like concurrency, synchronization, fault tolerance, and network protocols. The course includes hands-on labs using frameworks such as Apache Kafka and Docker.
Quantum Computing: This advanced elective explores quantum algorithms, quantum circuits, and quantum programming languages. Students will learn to simulate quantum systems and understand the potential impact of quantum computing on cryptography and optimization problems.
Embedded Systems: Focused on real-time systems and microcontroller-based applications, this course covers hardware-software co-design, interrupt handling, memory management, and device drivers. Practical labs involve programming ARM Cortex-M processors and developing embedded applications.
Project-Based Learning Framework
The department emphasizes project-based learning as a cornerstone of its academic philosophy. Mini-projects are introduced in the second year, allowing students to apply theoretical knowledge to practical scenarios. These projects typically last 4-6 weeks and require students to work in teams under faculty supervision.
Each mini-project is designed to reinforce concepts learned in core courses while encouraging creativity and innovation. Students must present their findings to a panel of experts and receive feedback for improvement. This process develops critical thinking, communication, and collaboration skills essential for professional success.
The final-year capstone project represents the culmination of students' academic journey. Projects are selected based on student interests, faculty expertise, and industry relevance. Students work closely with mentors to develop a comprehensive solution to a real-world problem or contribute to ongoing research initiatives.
Assessment criteria for projects include innovation, technical depth, presentation quality, and adherence to ethical standards. The department encourages interdisciplinary collaboration, allowing students to integrate knowledge from related fields such as biology, business, or physics into their projects.