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Comprehensive Course Catalogue

The following table outlines all courses offered across the 8 semesters of the Computer Science program at Adani University Ahmedabad, including course codes, titles, credit structures (L-T-P-C), and prerequisites.

Detailed Departmental Electives

Artificial Intelligence (CS301): This course introduces students to the fundamental concepts of AI, including search strategies, knowledge representation, planning, and reasoning. Students will explore various AI techniques such as neural networks, deep learning, and reinforcement learning. The course emphasizes real-world applications in robotics, computer vision, and natural language processing.

Machine Learning (CS302): Focused on the principles and algorithms of machine learning, this course covers supervised and unsupervised learning, decision trees, clustering, regression analysis, and ensemble methods. Students will gain hands-on experience with popular frameworks like TensorFlow and Scikit-learn.

Cybersecurity (CS303): This course delves into the principles of information security, including network security protocols, cryptography, ethical hacking, and digital forensics. It prepares students to develop secure systems and defend against cyber threats in real-world environments.

Web Technologies (CS304): Students learn modern web development practices, including HTML/CSS, JavaScript frameworks, RESTful APIs, and database integration. The course includes both frontend and backend technologies such as React, Node.js, and MongoDB.

Embedded Systems (CS305): This course focuses on designing and implementing systems that integrate computing capabilities into physical devices. Topics include microcontroller programming, real-time operating systems, sensor integration, and IoT applications.

Big Data Analytics (CS501): Students explore the tools and techniques used in processing large datasets using Hadoop, Spark, and other big data platforms. The course covers data mining, predictive modeling, and visualization techniques for extracting insights from massive datasets.

Natural Language Processing (CS502): This course introduces students to the computational methods used in analyzing human language. It covers text processing, sentiment analysis, named entity recognition, and machine translation using NLP libraries like NLTK and spaCy.

Computer Vision (CS503): Students learn how computers can interpret and understand visual information from images or videos. The course includes image filtering, object detection, feature extraction, and convolutional neural networks.

Mobile Application Development (CS504): This course teaches students to develop mobile apps for Android and iOS platforms using cross-platform frameworks like Flutter and React Native. It covers UI/UX design principles, app deployment, and performance optimization.

Computer Graphics (CS505): Students explore the fundamentals of computer graphics, including 3D modeling, rendering techniques, lighting models, and animation. The course includes practical implementation using OpenGL and Unity engine.

Advanced Algorithms (CS601): This course explores advanced algorithmic design and analysis, covering topics such as graph algorithms, dynamic programming, approximation algorithms, and complexity theory. Students will implement solutions to complex computational problems.

DevOps Practices (CS602): This course introduces students to continuous integration and delivery practices using tools like Jenkins, Docker, Kubernetes, and GitLab. It focuses on automating software development workflows and improving deployment efficiency.

Software Testing (CS603): Students learn various testing methodologies, including unit testing, integration testing, system testing, and performance testing. The course covers automated testing tools like Selenium and JUnit and emphasizes quality assurance practices.

System Design (CS604): This course teaches students how to design scalable and reliable software systems. It includes topics such as database design, caching strategies, load balancing, API design, and microservices architecture.

Digital Signal Processing (CS605): Students learn the principles of digital signal processing, including sampling theory, filtering techniques, Fourier transforms, and spectral analysis. Applications include audio processing, image enhancement, and biomedical signal analysis.

Project-Based Learning Philosophy

The department believes in immersive, hands-on learning experiences that bridge the gap between theory and practice. Project-based learning forms a core component of our curriculum, with students engaging in both individual and group projects throughout their academic journey.

In the first two years, students undertake mini-projects designed to reinforce classroom concepts and develop practical skills. These projects are typically completed over a period of 2–3 months and involve working with real datasets or developing small-scale applications. Faculty mentors provide guidance and feedback throughout the process.

During their third year, students begin working on more substantial projects that align with their chosen specialization track. The mini-project phase continues until the end of the fifth semester, culminating in a final-year thesis or capstone project that integrates all learned concepts.

The final-year thesis is a significant undertaking, requiring students to select a topic under the supervision of a faculty mentor. Students are encouraged to choose topics that address current industry challenges or contribute to ongoing research efforts. The project involves literature review, experimental design, implementation, and documentation. A public presentation and defense are required before the final submission.

Project selection is facilitated through an online portal where students can browse available projects based on their interests and faculty expertise. Students may propose their own ideas, subject to approval by the relevant department head or faculty mentor.