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.