Course Structure Overview
The curriculum for the B.Tech Computer Science program at Guru Nanak University Hyderabad is meticulously structured to provide a robust foundation followed by progressive specialization. The entire program spans eight semesters with each semester carrying specific credit structures and learning objectives.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming Using C | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Science | 4-0-0-4 | - |
| 1 | CS103 | Engineering Graphics | 2-0-0-2 | - |
| 1 | CS104 | Basic Electrical Engineering | 3-0-0-3 | - |
| 1 | CS105 | Communication Skills | 2-0-0-2 | - |
| 1 | CS106 | Computer Fundamentals | 3-0-0-3 | - |
| 2 | CS201 | Data Structures and Algorithms | 4-0-0-4 | CS101 |
| 2 | CS202 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 2 | CS203 | Digital Electronics | 3-0-0-3 | CS104 |
| 2 | CS204 | Object Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS205 | Database Management Systems | 3-0-0-3 | CS201 |
| 2 | CS206 | Probability and Statistics | 3-0-0-3 | CS102 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS204 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS201 |
| 3 | CS303 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS304 | Compiler Design | 3-0-0-3 | CS201 |
| 3 | CS305 | Web Technologies | 3-0-0-3 | CS204 |
| 3 | CS306 | Computer Architecture | 3-0-0-3 | CS203 |
| 4 | CS401 | Artificial Intelligence | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS302 |
| 4 | CS403 | Data Mining and Analytics | 3-0-0-3 | CS206 |
| 4 | CS404 | Distributed Systems | 3-0-0-3 | CS301 |
| 4 | CS405 | Mobile Application Development | 3-0-0-3 | CS305 |
| 4 | CS406 | Quantum Computing Concepts | 3-0-0-3 | CS201 |
| 5 | CS501 | Machine Learning | 3-0-0-3 | CS401 |
| 5 | CS502 | Deep Learning | 3-0-0-3 | CS501 |
| 5 | CS503 | Blockchain Technology | 3-0-0-3 | CS402 |
| 5 | CS504 | Internet of Things | 3-0-0-3 | CS302 |
| 5 | CS505 | Human-Computer Interaction | 3-0-0-3 | CS305 |
| 5 | CS506 | Big Data Technologies | 3-0-0-3 | CS403 |
| 6 | CS601 | Advanced Software Architecture | 3-0-0-3 | CS303 |
| 6 | CS602 | Security Protocols and Cryptography | 3-0-0-3 | CS402 |
| 6 | CS603 | Computer Vision | 3-0-0-3 | CS501 |
| 6 | CS604 | Natural Language Processing | 3-0-0-3 | CS501 |
| 6 | CS605 | Embedded Systems | 3-0-0-3 | CS306 |
| 6 | CS606 | Cloud Computing | 3-0-0-3 | CS404 |
| 7 | CS701 | Research Methodology | 2-0-0-2 | - |
| 7 | CS702 | Capstone Project I | 3-0-0-3 | CS601 |
| 7 | CS703 | Advanced Topics in AI | 3-0-0-3 | CS502 |
| 7 | CS704 | Advanced Cybersecurity | 3-0-0-3 | CS602 |
| 7 | CS705 | Specialized Elective I | 3-0-0-3 | - |
| 7 | CS706 | Specialized Elective II | 3-0-0-3 | - |
| 8 | CS801 | Capstone Project II | 6-0-0-6 | CS702 |
| 8 | CS802 | Internship | 3-0-0-3 | - |
| 8 | CS803 | Professional Ethics | 1-0-0-1 | - |
| 8 | CS804 | Entrepreneurship | 2-0-0-2 | - |
| 8 | CS805 | Specialized Elective III | 3-0-0-3 | - |
| 8 | CS806 | Specialized Elective IV | 3-0-0-3 | - |
Advanced Departmental Electives
The department offers a rich selection of advanced departmental electives that allow students to deepen their knowledge in specialized areas:
- Machine Learning: This course explores supervised and unsupervised learning algorithms, neural networks, deep learning architectures, reinforcement learning, and optimization techniques. Students learn to apply these methods to real-world problems across domains like healthcare, finance, and robotics.
- Deep Learning: Focused on building advanced neural network models using frameworks like TensorFlow and PyTorch, this course delves into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).
- Computer Vision: Students study image processing techniques, object detection, segmentation, feature extraction, and recognition algorithms. The course includes hands-on projects involving facial recognition systems, autonomous vehicles, and medical imaging.
- Natural Language Processing: This course covers text preprocessing, sentiment analysis, named entity recognition, machine translation, question answering systems, and chatbots using modern NLP libraries such as spaCy and Hugging Face Transformers.
- Cybersecurity Protocols: Students learn about encryption standards, authentication mechanisms, network security protocols, incident response strategies, and ethical hacking practices. Practical sessions involve penetration testing and vulnerability assessment tools.
- Blockchain Technology: This course examines blockchain architecture, smart contracts, consensus mechanisms, decentralized applications (dApps), cryptocurrency systems, and their implications in supply chain management, finance, and governance.
- Internet of Things: Students explore sensor networks, embedded systems programming, wireless communication protocols, cloud integration, and edge computing. Projects include developing IoT-based solutions for smart agriculture, healthcare monitoring, and urban infrastructure.
- Quantum Computing: Introduces quantum algorithms, qubits, superposition, entanglement, quantum gates, error correction, and simulation techniques. The course prepares students to understand the potential of quantum computers in solving complex optimization problems.
- Human-Computer Interaction: Focuses on usability principles, user experience design, prototyping, interaction design patterns, accessibility standards, and research methodologies for evaluating interface effectiveness.
- Big Data Technologies: Covers Hadoop ecosystem, Spark computing, NoSQL databases, data streaming platforms, and scalable data processing techniques. Students gain hands-on experience with tools like Apache Kafka, Hive, and Cassandra.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core pedagogical approach. From the second year onwards, students engage in mini-projects that reinforce theoretical concepts with practical implementation. These projects are typically completed in teams of 2-4 members and involve real-world scenarios.
Mini-projects span across various domains such as web development, mobile apps, AI models, cybersecurity simulations, database systems, and embedded devices. Each project is evaluated based on technical depth, innovation, presentation quality, and teamwork skills.
The final-year capstone project represents the culmination of all learning experiences. Students select topics relevant to their specialization or interest areas and work under the guidance of a faculty mentor. The project involves extensive research, system design, development, testing, documentation, and oral defense.
Project selection is facilitated through a proposal submission process where students present their ideas to faculty advisors. Mentorship ensures that projects are challenging yet achievable, with regular feedback sessions and milestone reviews throughout the duration of the project.