Curriculum Overview
The B.Tech Computer Science program at Icmai University Jaipur is structured to provide a comprehensive education that balances theoretical knowledge with practical application. The curriculum spans eight semesters, with each semester offering a blend of core courses, departmental electives, science electives, and laboratory sessions designed to foster innovation and critical thinking.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-0-0-3 | - |
| 1 | CS102 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CS103 | Basic Electrical Engineering | 3-0-0-3 | - |
| 1 | CS104 | Introduction to Programming using C/C++ | 2-0-2-2 | - |
| 1 | CS105 | Computer Organization | 3-0-0-3 | - |
| 1 | CS106 | Engineering Graphics & Design | 2-0-2-2 | - |
| 2 | CS201 | Engineering Mathematics II | 3-0-0-3 | CS101 |
| 2 | CS202 | Data Structures and Algorithms | 3-0-0-3 | CS104 |
| 2 | CS203 | Object-Oriented Programming | 2-0-2-2 | CS104 |
| 2 | CS204 | Database Management Systems | 3-0-0-3 | CS202 |
| 2 | CS205 | Computer Networks | 3-0-0-3 | CS105 |
| 2 | CS206 | Operating Systems | 3-0-0-3 | CS205 |
| 3 | CS301 | Software Engineering | 3-0-0-3 | CS203 |
| 3 | CS302 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CS202 |
| 3 | CS303 | Cybersecurity Fundamentals | 3-0-0-3 | CS205 |
| 3 | CS304 | Web Technologies | 2-0-2-2 | CS203 |
| 3 | CS305 | Embedded Systems | 3-0-0-3 | CS105 |
| 3 | CS306 | Compiler Design | 3-0-0-3 | CS202 |
| 4 | CS401 | Advanced Artificial Intelligence | 3-0-0-3 | CS302 |
| 4 | CS402 | Cloud Computing | 3-0-0-3 | CS205 |
| 4 | CS403 | Network Security | 3-0-0-3 | CS303 |
| 4 | CS404 | Big Data Analytics | 3-0-0-3 | CS204 |
| 4 | CS405 | Distributed Systems | 3-0-0-3 | CS205 |
| 4 | CS406 | Human-Computer Interaction | 3-0-0-3 | CS301 |
| 5 | CS501 | Deep Learning with TensorFlow | 3-0-0-3 | CS401 |
| 5 | CS502 | Blockchain Technology | 3-0-0-3 | CS303 |
| 5 | CS503 | Internet of Things (IoT) | 3-0-0-3 | CS305 |
| 5 | CS504 | Game Development | 3-0-0-3 | CS404 |
| 5 | CS505 | Computer Vision | 3-0-0-3 | CS401 |
| 5 | CS506 | Advanced Cryptography | 3-0-0-3 | CS303 |
| 6 | CS601 | Natural Language Processing | 3-0-0-3 | CS501 |
| 6 | CS602 | Reinforcement Learning | 3-0-0-3 | CS501 |
| 6 | CS603 | Edge Computing | 3-0-0-3 | CS402 |
| 6 | CS604 | Quantum Computing | 3-0-0-3 | CS201 |
| 6 | CS605 | Mobile Application Development | 3-0-0-3 | CS404 |
| 6 | CS606 | Software Architecture | 3-0-0-3 | CS301 |
| 7 | CS701 | Research Methodology | 2-0-0-2 | - |
| 7 | CS702 | Mini Project I | 2-0-0-2 | - |
| 7 | CS703 | Mini Project II | 2-0-0-2 | CS702 |
| 7 | CS704 | Capstone Project | 6-0-0-6 | CS703 |
| 7 | CS705 | Professional Ethics | 2-0-0-2 | - |
| 8 | CS801 | Final Year Thesis | 6-0-0-6 | CS704 |
| 8 | CS802 | Internship | 3-0-0-3 | - |
| 8 | CS803 | Industry Project | 6-0-0-6 | - |
| 8 | CS804 | Entrepreneurship & Innovation | 2-0-0-2 | - |
The curriculum is designed to ensure students gain a strong foundation in computer science fundamentals before progressing to specialized areas. Each course includes lectures, tutorials, and laboratory sessions that reinforce theoretical concepts with practical implementation.
Advanced Departmental Electives
Advanced departmental electives are offered in the final two years of study, allowing students to specialize in cutting-edge domains based on their interests and career aspirations. These courses are taught by faculty members who are active researchers and industry practitioners.
Deep Learning with TensorFlow: This course introduces students to deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement these models using TensorFlow and Keras, gaining hands-on experience in image recognition, natural language processing, and generative modeling.
Blockchain Technology: This elective explores the architecture and applications of blockchain systems. Topics include consensus mechanisms, smart contracts, cryptocurrency protocols, decentralized finance (DeFi), and enterprise blockchain solutions. Students develop projects using Ethereum and Hyperledger frameworks.
Internet of Things (IoT): Focused on sensor networks, wireless communication protocols, embedded systems programming, and cloud integration for IoT applications, this course prepares students to design and deploy smart devices in real-world environments. Practical sessions involve building prototypes using Raspberry Pi and Arduino platforms.
Game Development: This course combines programming, graphics, animation, and user experience design to create immersive gaming experiences. Students work with popular game engines like Unity and Unreal Engine, developing interactive applications and publishing them on various platforms.
Computer Vision: Covering image processing techniques, feature extraction algorithms, object detection, and recognition systems, this course teaches students how to build computer vision applications using OpenCV, TensorFlow, and PyTorch. Projects include facial recognition, autonomous vehicle navigation, and medical image analysis.
Advanced Cryptography: This course explores modern cryptographic techniques including public-key cryptography, hash functions, digital signatures, and secure multi-party computation. Students implement cryptographic protocols in real-world scenarios such as secure messaging systems and blockchain transactions.
Natural Language Processing: This elective delves into language modeling, sentiment analysis, machine translation, and text generation using transformer models like BERT and GPT. Students engage in NLP competitions and develop chatbots and intelligent assistants.
Reinforcement Learning: Students learn about Markov Decision Processes (MDPs), Q-learning, policy gradients, and actor-critic methods. Real-world applications include robotics control, game AI, and autonomous navigation systems. Projects involve training agents to play games like Chess or Go.
Edge Computing: This course covers distributed computing models where computation is performed at the edge of networks rather than centralized servers. Topics include fog computing, mobile cloud integration, and low-latency applications in smart cities and autonomous vehicles.
Quantum Computing: Designed for advanced students interested in quantum algorithms and hardware, this course introduces qubits, superposition, entanglement, and quantum gates. Students simulate quantum circuits using Python libraries like Qiskit and Cirq.
Mobile Application Development: This course focuses on building cross-platform mobile applications using frameworks like Flutter and React Native. Students learn to design responsive UIs, integrate APIs, and deploy apps to app stores.
Software Architecture: Emphasizing scalability, maintainability, and modularity in software systems, this course teaches architectural patterns such as microservices, event-driven architecture, and service mesh implementations. Students design and document enterprise-grade software solutions.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a means to bridge the gap between theory and practice. From the first year, students are exposed to mini-projects that challenge them to apply classroom knowledge in real-world contexts. These projects are carefully structured to promote collaboration, critical thinking, and innovation.
The structure of our project-based learning includes four key components: problem identification, research and planning, implementation, and evaluation. Students work in teams, often collaborating with faculty members or industry partners, ensuring that each project has a meaningful impact on real-world problems.
Mini-projects begin in the third semester and culminate in the seventh semester. The first mini-project involves developing a simple web application, followed by a more complex system in the second mini-project. These projects are evaluated using rubrics that assess technical skills, creativity, teamwork, and presentation abilities.
The final-year thesis or capstone project represents the culmination of a student’s academic journey. Students select topics aligned with their interests or industry needs, working closely with faculty mentors throughout the process. The project must demonstrate originality, depth, and practical applicability, often leading to publications, patents, or startup ideas.
Faculty mentors play a crucial role in guiding students through the project lifecycle. They provide expertise, resources, and feedback at every stage, ensuring that projects meet academic standards while remaining relevant and impactful. Regular meetings, progress reviews, and milestone assessments help maintain quality and ensure timely completion.