Curriculum Overview
The Computer Science curriculum at Transstadia University Ahmedabad is meticulously designed to provide a comprehensive and rigorous education that balances theoretical knowledge with practical application. The program spans four years and consists of eight semesters, with a structured progression from foundational subjects to advanced specializations.
The curriculum is divided into core courses, departmental electives, science electives, and laboratory components. Core courses form the backbone of the program, providing students with essential knowledge in mathematics, physics, computer science fundamentals, and engineering principles. Departmental electives allow students to explore specialized areas of interest, while science electives broaden their understanding of related disciplines. Laboratory components are integral to the curriculum, offering hands-on experience with cutting-edge technologies and tools.
Semester-wise Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | None |
| 1 | CS102 | Mathematics for Computer Science | 4-0-0-4 | None |
| 1 | CS103 | Physics for Computer Science | 3-0-0-3 | None |
| 1 | CS104 | Engineering Drawing | 2-0-0-2 | None |
| 1 | CS105 | Communication Skills | 2-0-0-2 | None |
| 1 | CS106 | Computer Laboratory | 0-0-3-1 | None |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 2 | CS203 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS204 | Database Systems | 3-0-0-3 | CS101 |
| 2 | CS205 | Computer Organization | 3-0-0-3 | CS103 |
| 2 | CS206 | Computer Laboratory | 0-0-3-1 | CS101 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS201 |
| 3 | CS303 | Software Engineering | 3-0-0-3 | CS201 |
| 3 | CS304 | Design and Analysis of Algorithms | 3-0-0-3 | CS201 |
| 3 | CS305 | Artificial Intelligence | 3-0-0-3 | CS201 |
| 3 | CS306 | Computer Laboratory | 0-0-3-1 | CS201 |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity | 3-0-0-3 | CS301 |
| 4 | CS403 | Data Science | 3-0-0-3 | CS301 |
| 4 | CS404 | Human-Computer Interaction | 3-0-0-3 | CS301 |
| 4 | CS405 | Embedded Systems | 3-0-0-3 | CS301 |
| 4 | CS406 | Capstone Project | 0-0-6-3 | CS301 |
| 5 | CS501 | Advanced Algorithms | 3-0-0-3 | CS304 |
| 5 | CS502 | Big Data Analytics | 3-0-0-3 | CS304 |
| 5 | CS503 | Cloud Computing | 3-0-0-3 | CS301 |
| 5 | CS504 | Computer Vision | 3-0-0-3 | CS301 |
| 5 | CS505 | Blockchain Technology | 3-0-0-3 | CS301 |
| 5 | CS506 | Research Project | 0-0-6-3 | CS301 |
| 6 | CS601 | Advanced Cybersecurity | 3-0-0-3 | CS402 |
| 6 | CS602 | Deep Learning | 3-0-0-3 | CS401 |
| 6 | CS603 | Quantitative Finance | 3-0-0-3 | CS304 |
| 6 | CS604 | Mobile Application Development | 3-0-0-3 | CS301 |
| 6 | CS605 | Internet of Things | 3-0-0-3 | CS301 |
| 6 | CS606 | Capstone Project | 0-0-6-3 | CS301 |
| 7 | CS701 | Special Topics in AI | 3-0-0-3 | CS401 |
| 7 | CS702 | Advanced Data Science | 3-0-0-3 | CS403 |
| 7 | CS703 | Advanced Computer Graphics | 3-0-0-3 | CS301 |
| 7 | CS704 | Software Architecture | 3-0-0-3 | CS303 |
| 7 | CS705 | Research Seminar | 0-0-3-1 | CS301 |
| 7 | CS706 | Internship | 0-0-0-3 | CS301 |
| 8 | CS801 | Advanced Research Project | 0-0-6-3 | CS301 |
| 8 | CS802 | Capstone Project | 0-0-6-3 | CS301 |
| 8 | CS803 | Professional Development | 0-0-3-1 | CS301 |
| 8 | CS804 | Graduation Thesis | 0-0-6-3 | CS301 |
Advanced Departmental Electives
Advanced departmental electives provide students with specialized knowledge in emerging areas of Computer Science. These courses are designed to deepen students' understanding of specific domains and prepare them for advanced research or industry roles.
Machine Learning
This course covers advanced topics in machine learning, including deep learning, reinforcement learning, and neural networks. Students will learn to implement and evaluate machine learning models using frameworks like TensorFlow and PyTorch. The course emphasizes practical applications and real-world problem-solving.
Cybersecurity
This course provides a comprehensive overview of cybersecurity principles and practices. Students will study network security, cryptography, ethical hacking, and risk management. The course includes hands-on labs and case studies to enhance practical understanding.
Data Science
This course focuses on data analysis, statistical modeling, and machine learning techniques. Students will learn to use tools like Python, R, and SQL to analyze large datasets and extract meaningful insights. The course emphasizes data visualization and communication of results.
Human-Computer Interaction
This course explores the design and evaluation of interactive systems. Students will study user experience design, usability testing, and interface development. The course includes practical projects where students create prototypes and conduct user studies.
Embedded Systems
This course covers the design and programming of embedded systems. Students will study microcontrollers, real-time systems, and sensor networks. The course includes hands-on projects involving hardware and software integration.
Computer Vision
This course focuses on image processing, pattern recognition, and computer vision applications. Students will learn to implement computer vision algorithms using tools like OpenCV and TensorFlow. The course emphasizes practical applications in robotics, surveillance, and medical imaging.
Cloud Computing
This course provides an in-depth understanding of cloud computing platforms and services. Students will study virtualization, distributed systems, and cloud architecture. The course includes hands-on experience with cloud platforms like AWS and Azure.
Blockchain Technology
This course explores the technology behind blockchain and distributed ledgers. Students will study cryptocurrency, smart contracts, and decentralized applications. The course includes practical projects involving blockchain development and implementation.
Internet of Things
This course covers the design and implementation of IoT systems. Students will study sensor networks, wireless communication, and embedded systems. The course emphasizes practical applications in smart cities, agriculture, and healthcare.
Advanced Algorithms
This course focuses on advanced algorithmic techniques and complexity analysis. Students will study graph algorithms, optimization methods, and approximation algorithms. The course emphasizes problem-solving and algorithm design.
Project-Based Learning
Project-based learning is a cornerstone of the Computer Science program at Transstadia University Ahmedabad. This approach emphasizes hands-on experience and real-world problem-solving, preparing students for professional careers in the field.
The program includes mandatory mini-projects in the second and third years, followed by a comprehensive final-year thesis or capstone project. Mini-projects are designed to reinforce concepts learned in core courses and provide students with practical experience in software development, research, and problem-solving.
The final-year capstone project is a significant undertaking that allows students to integrate knowledge from multiple disciplines and apply it to a real-world challenge. Students work in teams to develop innovative solutions to complex problems, often in collaboration with industry partners.
Students select their projects and mentors based on their interests and career goals. Faculty members provide guidance and support throughout the project lifecycle, ensuring that students receive the necessary resources and expertise to succeed.
The evaluation criteria for projects include technical excellence, innovation, presentation, and teamwork. Students are assessed on their ability to design, implement, and document their solutions, as well as their capacity to communicate their work effectively.