Collegese

Welcome to Collegese! Sign in →

Collegese
  • Colleges
  • Courses
  • Exams
  • Scholarships
  • Blog

Search colleges and courses

Search and navigate to colleges and courses

Start your journey

Ready to find your dream college?

Join thousands of students making smarter education decisions.

Watch How It WorksGet Started

Discover

Browse & filter colleges

Compare

Side-by-side analysis

Explore

Detailed course info

Collegese

India's education marketplace helping students discover the right colleges, compare courses, and build careers they deserve.

© 2026 Collegese. All rights reserved. A product of Nxthub Consulting Pvt. Ltd.

Apply

Scholarships & exams

support@collegese.com
+91 88943 57155
Pune, Maharashtra, India

Duration

4 Years

Computer Science

Transstadia University Ahmedabad
Duration
4 Years
Computer Science UG OFFLINE

Duration

4 Years

Computer Science

Transstadia University Ahmedabad
Duration
Apply

Fees

₹12,00,000

Placement

92.0%

Avg Package

₹5,00,000

Highest Package

₹8,00,000

OverviewAdmissionsCurriculumFeesPlacements
4 Years
Computer Science
UG
OFFLINE

Fees

₹12,00,000

Placement

92.0%

Avg Package

₹5,00,000

Highest Package

₹8,00,000

Seats

120

Students

1,200

ApplyCollege

Seats

120

Students

1,200

Curriculum

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

SemesterCourse CodeCourse TitleCredit (L-T-P-C)Prerequisites
1CS101Introduction to Programming3-0-0-3None
1CS102Mathematics for Computer Science4-0-0-4None
1CS103Physics for Computer Science3-0-0-3None
1CS104Engineering Drawing2-0-0-2None
1CS105Communication Skills2-0-0-2None
1CS106Computer Laboratory0-0-3-1None
2CS201Data Structures and Algorithms3-0-0-3CS101
2CS202Discrete Mathematics3-0-0-3CS102
2CS203Object-Oriented Programming3-0-0-3CS101
2CS204Database Systems3-0-0-3CS101
2CS205Computer Organization3-0-0-3CS103
2CS206Computer Laboratory0-0-3-1CS101
3CS301Operating Systems3-0-0-3CS201
3CS302Computer Networks3-0-0-3CS201
3CS303Software Engineering3-0-0-3CS201
3CS304Design and Analysis of Algorithms3-0-0-3CS201
3CS305Artificial Intelligence3-0-0-3CS201
3CS306Computer Laboratory0-0-3-1CS201
4CS401Machine Learning3-0-0-3CS301
4CS402Cybersecurity3-0-0-3CS301
4CS403Data Science3-0-0-3CS301
4CS404Human-Computer Interaction3-0-0-3CS301
4CS405Embedded Systems3-0-0-3CS301
4CS406Capstone Project0-0-6-3CS301
5CS501Advanced Algorithms3-0-0-3CS304
5CS502Big Data Analytics3-0-0-3CS304
5CS503Cloud Computing3-0-0-3CS301
5CS504Computer Vision3-0-0-3CS301
5CS505Blockchain Technology3-0-0-3CS301
5CS506Research Project0-0-6-3CS301
6CS601Advanced Cybersecurity3-0-0-3CS402
6CS602Deep Learning3-0-0-3CS401
6CS603Quantitative Finance3-0-0-3CS304
6CS604Mobile Application Development3-0-0-3CS301
6CS605Internet of Things3-0-0-3CS301
6CS606Capstone Project0-0-6-3CS301
7CS701Special Topics in AI3-0-0-3CS401
7CS702Advanced Data Science3-0-0-3CS403
7CS703Advanced Computer Graphics3-0-0-3CS301
7CS704Software Architecture3-0-0-3CS303
7CS705Research Seminar0-0-3-1CS301
7CS706Internship0-0-0-3CS301
8CS801Advanced Research Project0-0-6-3CS301
8CS802Capstone Project0-0-6-3CS301
8CS803Professional Development0-0-3-1CS301
8CS804Graduation Thesis0-0-6-3CS301

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.