Curriculum Overview
The Computer Applications program at Shri Jagdish Prasad Jhabarmal Tibrewala University is structured to provide a comprehensive and progressive learning experience over four years. The curriculum is designed to balance theoretical knowledge with practical application, ensuring that students are well-prepared for both industry roles and further academic pursuits.
The program is divided into eight semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions. The total credit hours for the program are 180, distributed across these semesters to ensure a balanced workload and effective learning outcomes.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Computing | 3-0-0-3 | None |
| 1 | CS102 | Programming in C | 3-0-0-3 | None |
| 1 | CS103 | Mathematics I | 3-0-0-3 | None |
| 1 | CS104 | Physics I | 3-0-0-3 | None |
| 1 | CS105 | Chemistry I | 3-0-0-3 | None |
| 1 | CS106 | English Communication | 3-0-0-3 | None |
| 1 | CS107 | Computer Lab I | 0-0-3-1 | None |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS102 |
| 2 | CS202 | Object-Oriented Programming | 3-0-0-3 | CS102 |
| 2 | CS203 | Mathematics II | 3-0-0-3 | CS103 |
| 2 | CS204 | Physics II | 3-0-0-3 | CS104 |
| 2 | CS205 | Electronics | 3-0-0-3 | CS104 |
| 2 | CS206 | Communication Skills | 3-0-0-3 | CS106 |
| 2 | CS207 | Computer Lab II | 0-0-3-1 | CS107 |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS202 |
| 3 | CS303 | Operating Systems | 3-0-0-3 | CS202 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS201 |
| 3 | CS305 | Mathematics III | 3-0-0-3 | CS203 |
| 3 | CS306 | Web Technologies | 3-0-0-3 | CS202 |
| 3 | CS307 | Computer Lab III | 0-0-3-1 | CS207 |
| 4 | CS401 | Artificial Intelligence | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity | 3-0-0-3 | CS302 |
| 4 | CS403 | Cloud Computing | 3-0-0-3 | CS302 |
| 4 | CS404 | Mobile Application Development | 3-0-0-3 | CS306 |
| 4 | CS405 | Human-Computer Interaction | 3-0-0-3 | CS304 |
| 4 | CS406 | Data Science | 3-0-0-3 | CS301 |
| 4 | CS407 | Computer Lab IV | 0-0-3-1 | CS307 |
| 5 | CS501 | Advanced Machine Learning | 3-0-0-3 | CS401 |
| 5 | CS502 | Network Security | 3-0-0-3 | CS402 |
| 5 | CS503 | DevOps and CI/CD | 3-0-0-3 | CS403 |
| 5 | CS504 | Game Development | 3-0-0-3 | CS404 |
| 5 | CS505 | Big Data Analytics | 3-0-0-3 | CS406 |
| 5 | CS506 | Internet of Things | 3-0-0-3 | CS403 |
| 5 | CS507 | Computer Lab V | 0-0-3-1 | CS407 |
| 6 | CS601 | Deep Learning | 3-0-0-3 | CS501 |
| 6 | CS602 | Blockchain Technology | 3-0-0-3 | CS502 |
| 6 | CS603 | Mobile Security | 3-0-0-3 | CS504 |
| 6 | CS604 | Embedded Systems | 3-0-0-3 | CS506 |
| 6 | CS605 | Research Methodology | 3-0-0-3 | CS505 |
| 6 | CS606 | Computer Lab VI | 0-0-3-1 | CS507 |
| 7 | CS701 | Capstone Project I | 3-0-0-3 | CS605 |
| 7 | CS702 | Industry Internship | 0-0-0-3 | CS605 |
| 7 | CS703 | Entrepreneurship | 3-0-0-3 | CS605 |
| 7 | CS704 | Advanced Software Engineering | 3-0-0-3 | CS404 |
| 7 | CS705 | Computer Lab VII | 0-0-3-1 | CS606 |
| 8 | CS801 | Capstone Project II | 3-0-0-3 | CS701 |
| 8 | CS802 | Research Thesis | 0-0-0-6 | CS701 |
| 8 | CS803 | Professional Development | 3-0-0-3 | CS703 |
| 8 | CS804 | Computer Lab VIII | 0-0-3-1 | CS705 |
Advanced Departmental Electives
The department offers several advanced elective courses that allow students to specialize in specific areas of interest. These courses are designed to provide in-depth knowledge and practical skills in cutting-edge technologies.
Advanced Machine Learning: This course explores advanced topics in machine learning, including deep learning, reinforcement learning, and neural architecture search. Students will work on real-world datasets and develop models for complex applications such as natural language processing and computer vision. The course includes hands-on labs and projects that utilize frameworks like TensorFlow and PyTorch.
Network Security: This course covers the principles and practices of network security, including encryption, authentication, and intrusion detection. Students will learn about secure network design, vulnerability assessment, and incident response. The course includes practical labs where students simulate network attacks and implement security measures.
DevOps and CI/CD: This course focuses on continuous integration and continuous delivery practices in software development. Students will learn about automation tools like Jenkins, Docker, and Kubernetes, and how to implement DevOps pipelines. The course includes hands-on labs and projects that simulate real-world software development environments.
Game Development: This course covers the fundamentals of game development, including game design, graphics programming, and user interface design. Students will work on creating 2D and 3D games using engines like Unity and Unreal Engine. The course includes practical projects where students develop their own games from concept to completion.
Big Data Analytics: This course introduces students to big data technologies and analytics techniques. Students will learn about Hadoop, Spark, and NoSQL databases, and how to analyze large datasets using statistical and machine learning methods. The course includes hands-on labs and projects that involve real-world big data applications.
Internet of Things: This course explores the principles and applications of IoT, including sensor networks, embedded systems, and smart devices. Students will learn about IoT architectures, protocols, and security considerations. The course includes practical labs where students build IoT applications using platforms like Arduino and Raspberry Pi.
Deep Learning: This course delves into advanced deep learning techniques, including convolutional neural networks, recurrent neural networks, and transformer models. Students will work on projects involving image recognition, natural language processing, and generative models. The course includes hands-on labs and projects that utilize frameworks like TensorFlow and PyTorch.
Blockchain Technology: This course covers the fundamentals of blockchain technology and its applications in various industries. Students will learn about consensus mechanisms, smart contracts, and decentralized applications. The course includes practical labs where students develop blockchain applications using platforms like Ethereum and Hyperledger.
Mobile Security: This course focuses on security challenges in mobile applications and how to address them. Students will learn about mobile threats, secure coding practices, and mobile security frameworks. The course includes practical labs where students analyze and secure mobile applications.
Embedded Systems: This course explores the design and implementation of embedded systems, including microcontrollers, real-time operating systems, and hardware-software integration. Students will work on projects involving embedded system development using platforms like ARM and AVR. The course includes hands-on labs and projects that simulate real-world embedded system applications.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered on providing students with real-world experience and practical skills. Projects are designed to simulate industry challenges and require students to apply theoretical knowledge to solve complex problems.
Mini-projects are introduced in the second and third years of the program. These projects are typically completed in groups and focus on specific aspects of the curriculum. For example, students may develop a simple database application, create a basic web application, or implement a data structure in a practical context. These projects are evaluated based on technical implementation, documentation, and presentation.
The final-year thesis/capstone project is a significant component of the program. Students work on a comprehensive project that integrates knowledge from all areas of the curriculum. The project is typically completed in a team of 3-5 students and is supervised by a faculty member. The project involves problem identification, research, design, implementation, testing, and documentation.
Students select their projects based on their interests and career goals, with guidance from faculty mentors. The selection process involves a proposal submission, where students present their project idea, objectives, and methodology. Faculty mentors are assigned based on their expertise and availability, ensuring that students receive appropriate guidance throughout the project.
Evaluation criteria for projects include technical depth, innovation, documentation quality, presentation skills, and teamwork. Students are assessed on their ability to apply theoretical knowledge, solve practical problems, and communicate their findings effectively. The final evaluation includes a project presentation, a written report, and a demonstration of the implemented solution.