Course Structure and Academic Framework
The Computer Science program at Maya Institute Of Technology And Management is designed to provide a comprehensive and progressive learning experience over four years. The curriculum is structured into core subjects, departmental electives, science electives, and laboratory sessions that collectively build a strong foundation in both theoretical concepts and practical applications.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Computing | 3-1-0-4 | None |
| I | CS102 | Programming in C | 3-1-0-4 | None |
| I | CS103 | Mathematics for Computer Science I | 3-1-0-4 | None |
| I | CS104 | Physics for Computing | 3-1-0-4 | None |
| I | CS105 | Chemistry for Computing | 3-1-0-4 | None |
| I | CS106 | English for Technical Communication | 3-1-0-4 | None |
| I | CS107 | Computer Organization and Architecture | 3-1-0-4 | None |
| I | CS108 | Introduction to Data Structures and Algorithms | 3-1-0-4 | None |
| I | CS109 | Lab Session for Programming in C | 0-0-3-1 | None |
| I | CS110 | Lab Session for Computer Organization and Architecture | 0-0-3-1 | None |
| I | CS111 | Lab Session for Introduction to Data Structures and Algorithms | 0-0-3-1 | None |
| I | CS112 | Lab Session for Mathematics for Computer Science I | 0-0-3-1 | None |
| I | CS113 | Lab Session for Physics for Computing | 0-0-3-1 | None |
| I | CS114 | Lab Session for Chemistry for Computing | 0-0-3-1 | None |
| I | CS115 | Lab Session for English for Technical Communication | 0-0-3-1 | None |
| II | CS201 | Data Structures and Algorithms II | 3-1-0-4 | CS108 |
| II | CS202 | Object-Oriented Programming in Java | 3-1-0-4 | None |
| II | CS203 | Mathematics for Computer Science II | 3-1-0-4 | CS103 |
| II | CS204 | Database Systems | 3-1-0-4 | None |
| II | CS205 | Operating Systems | 3-1-0-4 | None |
| II | CS206 | Computer Networks | 3-1-0-4 | None |
| II | CS207 | Software Engineering | 3-1-0-4 | None |
| II | CS208 | Discrete Mathematics | 3-1-0-4 | None |
| II | CS209 | Lab Session for Data Structures and Algorithms II | 0-0-3-1 | CS201 |
| II | CS210 | Lab Session for Object-Oriented Programming in Java | 0-0-3-1 | CS202 |
| II | CS211 | Lab Session for Database Systems | 0-0-3-1 | CS204 |
| II | CS212 | Lab Session for Operating Systems | 0-0-3-1 | CS205 |
| II | CS213 | Lab Session for Computer Networks | 0-0-3-1 | CS206 |
| II | CS214 | Lab Session for Software Engineering | 0-0-3-1 | CS207 |
| II | CS215 | Lab Session for Discrete Mathematics | 0-0-3-1 | CS208 |
| III | CS301 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS201, CS202 |
| III | CS302 | Cybersecurity Fundamentals | 3-1-0-4 | CS205, CS206 |
| III | CS303 | Data Science and Analytics | 3-1-0-4 | CS201, CS203 |
| III | CS304 | Human-Computer Interaction | 3-1-0-4 | None |
| III | CS305 | Distributed Systems | 3-1-0-4 | CS205, CS206 |
| III | CS306 | Mobile Application Development | 3-1-0-4 | CS202 |
| III | CS307 | Internet of Things (IoT) | 3-1-0-4 | CS206, CS207 |
| III | CS308 | Quantum Computing | 3-1-0-4 | CS203, CS205 |
| III | CS309 | Lab Session for Artificial Intelligence and Machine Learning | 0-0-3-1 | CS301 |
| III | CS310 | Lab Session for Cybersecurity Fundamentals | 0-0-3-1 | CS302 |
| III | CS311 | Lab Session for Data Science and Analytics | 0-0-3-1 | CS303 |
| III | CS312 | Lab Session for Human-Computer Interaction | 0-0-3-1 | CS304 |
| III | CS313 | Lab Session for Distributed Systems | 0-0-3-1 | CS305 |
| III | CS314 | Lab Session for Mobile Application Development | 0-0-3-1 | CS306 |
| III | CS315 | Lab Session for Internet of Things (IoT) | 0-0-3-1 | CS307 |
| III | CS316 | Lab Session for Quantum Computing | 0-0-3-1 | CS308 |
| IV | CS401 | Capstone Project - Artificial Intelligence | 3-1-0-4 | CS301, CS302 |
| IV | CS402 | Capstone Project - Cybersecurity | 3-1-0-4 | CS302, CS305 |
| IV | CS403 | Capstone Project - Data Science | 3-1-0-4 | CS303, CS305 |
| IV | CS404 | Capstone Project - Human-Computer Interaction | 3-1-0-4 | CS304, CS306 |
| IV | CS405 | Capstone Project - Mobile Application Development | 3-1-0-4 | CS306, CS307 |
| IV | CS406 | Capstone Project - Internet of Things (IoT) | 3-1-0-4 | CS307, CS308 |
| IV | CS407 | Capstone Project - Quantum Computing | 3-1-0-4 | CS308, CS305 |
| IV | CS408 | Final Year Thesis | 3-1-0-4 | All previous courses |
Advanced Departmental Electives
The department offers a wide range of advanced departmental electives designed to provide students with specialized knowledge in cutting-edge areas of Computer Science. These courses are tailored for students who wish to deepen their expertise in specific domains.
- Advanced Machine Learning: This course explores deep learning architectures, reinforcement learning, and advanced neural network models. Students will learn about transformer-based architectures, generative adversarial networks, and large language models. The course includes hands-on projects using frameworks like TensorFlow and PyTorch.
- Cryptography and Network Security: This elective covers modern cryptographic techniques, secure communication protocols, and advanced network security mechanisms. Students will study topics such as public-key infrastructure, digital signatures, and blockchain-based security systems.
- Big Data Analytics: The course introduces students to big data technologies and tools like Hadoop, Spark, and NoSQL databases. It focuses on data processing, analysis, and visualization techniques for large-scale datasets.
- Computer Vision: This course explores image processing, object detection, facial recognition, and computer vision applications. Students will learn to develop algorithms for autonomous vehicles, medical imaging, and augmented reality systems.
- Game Development: Designed for students interested in creating interactive entertainment, this elective covers game design principles, 3D modeling, physics simulation, and real-time rendering techniques using engines like Unity and Unreal Engine.
- Cloud Computing: This course introduces cloud architecture, virtualization, containerization, and serverless computing. Students will gain experience with platforms like AWS, Azure, and Google Cloud through practical labs and projects.
- Blockchain Technology: The course explores blockchain fundamentals, smart contracts, decentralized applications (dApps), and cryptocurrency systems. Students will develop their own blockchain-based applications using Ethereum and Hyperledger frameworks.
- Mobile App Development: This elective focuses on building cross-platform mobile applications using React Native and Flutter. Students will learn about app design, user experience, and integration with backend services.
- Natural Language Processing: This course covers linguistic analysis, text classification, sentiment analysis, and language generation techniques. It includes practical projects involving chatbots, translation systems, and speech recognition applications.
- Embedded Systems: Students will study real-time operating systems, microcontroller programming, and hardware-software integration. The course emphasizes designing embedded solutions for IoT devices and robotics applications.
Project-Based Learning Philosophy
The department strongly believes in the power of project-based learning as a means to develop practical skills and foster innovation among students. This approach ensures that theoretical knowledge is applied in real-world contexts, preparing students for professional environments.
Mini-projects are assigned throughout the academic year, starting from the first semester. These projects allow students to apply fundamental concepts learned in class to solve small-scale problems. The mini-projects are evaluated based on technical implementation, creativity, and documentation quality.
The final-year capstone project is a significant component of the program. Students work in teams or individually under faculty mentorship to develop innovative solutions to complex real-world challenges. The projects often involve collaboration with industry partners, providing students with exposure to professional standards and expectations.
Students are encouraged to select projects based on their interests and career goals. Faculty mentors guide students through the process, helping them refine ideas, choose appropriate technologies, and manage project timelines effectively.
The evaluation criteria for these projects include technical feasibility, innovation, impact, presentation skills, and team collaboration. Students must submit detailed reports, demonstrate their work, and present findings to a panel of faculty members and industry experts.