Curriculum Overview
The curriculum at Maulana Azad University Jodhpur for Computer Science is designed to provide a robust foundation in both theoretical and practical aspects of computing. It balances core disciplines with specialized electives, ensuring students are well-prepared for diverse career paths.
The program spans eight semesters, each carefully structured to build upon previous knowledge while introducing new concepts. From foundational courses in mathematics and programming to advanced topics in artificial intelligence and cybersecurity, the curriculum reflects the latest trends in the field.
Course Structure
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-2-4 | None |
| I | MA101 | Mathematics for Computer Science | 3-0-0-3 | None |
| I | PH101 | Physics for Engineers | 3-0-0-3 | None |
| I | CH101 | Chemistry for Engineers | 3-0-0-3 | None |
| I | EC101 | Electrical Circuits and Electronics | 3-0-0-3 | None |
| I | HS101 | English for Communication | 2-0-0-2 | None |
| I | CS102 | Programming Laboratory | 0-0-4-2 | CS101 |
| I | MA102 | Discrete Mathematics | 3-0-0-3 | MA101 |
| II | CS201 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| II | MA201 | Probability and Statistics | 3-0-0-3 | MA101 |
| II | PH201 | Modern Physics | 3-0-0-3 | PH101 |
| II | CH201 | Organic Chemistry | 3-0-0-3 | CH101 |
| II | EC201 | Digital Electronics | 3-0-0-3 | EC101 |
| II | HS201 | Cultural Studies | 2-0-0-2 | None |
| II | CS202 | Lab: Data Structures and Algorithms | 0-0-4-2 | CS201 |
| III | CS301 | Database Management Systems | 3-0-2-4 | CS201 |
| III | MA301 | Linear Algebra and Numerical Methods | 3-0-0-3 | MA102 |
| III | PH301 | Optics and Thermodynamics | 3-0-0-3 | PH201 |
| III | CH301 | Inorganic Chemistry | 3-0-0-3 | CH201 |
| III | EC301 | Signals and Systems | 3-0-0-3 | EC201 |
| III | CS302 | Operating Systems | 3-0-2-4 | CS201 |
| III | CS303 | Lab: Operating Systems | 0-0-4-2 | CS302 |
| IV | CS401 | Computer Networks | 3-0-2-4 | CS302 |
| IV | MA401 | Differential Equations | 3-0-0-3 | MA301 |
| IV | PH401 | Quantum Physics | 3-0-0-3 | PH301 |
| IV | CH401 | Physical Chemistry | 3-0-0-3 | CH301 |
| IV | EC401 | Analog Electronics | 3-0-0-3 | EC301 |
| IV | CS402 | Software Engineering | 3-0-2-4 | CS301 |
| IV | CS403 | Lab: Software Engineering | 0-0-4-2 | CS402 |
| V | CS501 | Artificial Intelligence | 3-0-2-4 | CS402 |
| V | MA501 | Advanced Calculus | 3-0-0-3 | MA401 |
| V | PH501 | Electromagnetism | 3-0-0-3 | PH401 |
| V | CH501 | Chemistry of Materials | 3-0-0-3 | CH401 |
| V | EC501 | Microprocessors and Microcontrollers | 3-0-0-3 | EC401 |
| V | CS502 | Cybersecurity Fundamentals | 3-0-2-4 | CS401 |
| V | CS503 | Lab: Cybersecurity | 0-0-4-2 | CS502 |
| VI | CS601 | Machine Learning | 3-0-2-4 | CS501 |
| VI | MA601 | Stochastic Processes | 3-0-0-3 | MA501 |
| VI | PH601 | Condensed Matter Physics | 3-0-0-3 | PH501 |
| VI | CH601 | Organometallic Chemistry | 3-0-0-3 | CH501 |
| VI | EC601 | Control Systems | 3-0-0-3 | EC501 |
| VI | CS602 | Big Data Analytics | 3-0-2-4 | CS501 |
| VI | CS603 | Lab: Big Data Analytics | 0-0-4-2 | CS602 |
| VII | CS701 | Advanced Computer Architecture | 3-0-2-4 | CS501 |
| VII | MA701 | Mathematical Modeling | 3-0-0-3 | MA601 |
| VII | PH701 | Nuclear Physics | 3-0-0-3 | PH601 |
| VII | CH701 | Physical Organic Chemistry | 3-0-0-3 | CH601 |
| VII | EC701 | Communication Systems | 3-0-0-3 | EC601 |
| VII | CS702 | Human Computer Interaction | 3-0-2-4 | CS601 |
| VII | CS703 | Lab: Human Computer Interaction | 0-0-4-2 | CS702 |
| VIII | CS801 | Capstone Project | 0-0-8-8 | All previous semesters |
| VIII | MA801 | Research Methodology | 3-0-0-3 | None |
| VIII | PH801 | Quantum Computing | 3-0-0-3 | PH701 |
| VIII | CH801 | Chemical Biology | 3-0-0-3 | CH701 |
| VIII | EC801 | Signal Processing | 3-0-0-3 | EC701 |
| VIII | CS802 | Entrepreneurship and Innovation | 2-0-0-2 | None |
| VIII | CS803 | Internship | 0-0-6-6 | None |
Advanced Departmental Electives
The department offers a wide array of advanced elective courses designed to deepen students' understanding of specialized areas within Computer Science:
- Deep Learning for Vision and Language: This course explores the architecture and implementation of neural networks for image recognition, natural language processing, and multimodal tasks. Students learn to build models using frameworks like TensorFlow and PyTorch, applying them to real-world datasets.
- Advanced Cryptography and Network Security: Covering advanced topics in cryptography including elliptic curve encryption, hash functions, and secure protocols. The course also examines current threats and defensive strategies used by modern organizations.
- Cloud Computing and DevOps: Students learn about cloud platforms like AWS, Azure, and GCP, along with automation tools such as Jenkins, Docker, and Kubernetes. The curriculum includes designing scalable architectures and implementing CI/CD pipelines.
- Human-Computer Interaction Design: Focused on user-centered design principles, this course teaches students to create interfaces that are intuitive, accessible, and effective. It covers usability testing, prototyping techniques, and cognitive psychology aspects of interaction design.
- Internet of Things (IoT) Systems: This course introduces students to sensor networks, embedded systems programming, and wireless communication protocols used in IoT applications. Practical labs involve building IoT prototypes using Raspberry Pi and Arduino boards.
- Game Development Fundamentals: Students learn the entire game development lifecycle from concept creation to implementation using Unity and Unreal Engine. The course includes scripting, asset design, and performance optimization techniques.
- Mobile Application Development: This course focuses on developing cross-platform mobile applications for iOS and Android using technologies like React Native and Flutter. It covers UI/UX design, backend integration, and app deployment strategies.
- Data Mining and Knowledge Discovery: Students explore algorithms for extracting patterns from large datasets. Topics include clustering, classification, association rules, and anomaly detection, with practical applications in business intelligence and scientific research.
- Quantum Computing Fundamentals: Introducing students to the principles of quantum mechanics and how they apply to computing. The course covers quantum gates, superposition, entanglement, and current developments in quantum algorithms and hardware.
- Computational Biology: This interdisciplinary course combines computer science with biology, focusing on bioinformatics tools and computational methods used in genomics, proteomics, and drug discovery.
Project-Based Learning Philosophy
The department's philosophy on project-based learning emphasizes hands-on experience and real-world problem-solving. Mini-projects are assigned at the end of each semester, allowing students to apply theoretical knowledge to practical scenarios. These projects are evaluated based on innovation, implementation quality, and presentation skills.
The final-year capstone project is a significant component of the curriculum, requiring students to work in teams on an industry-relevant problem. Students select their projects based on personal interest and faculty mentorship availability. The evaluation criteria include technical depth, originality, documentation, and oral defense. Projects are often presented at national and international conferences, providing exposure to real-world applications.