Course Structure Overview
The B.Tech Computer Science program at Major S D Singh University Farrukhabad is meticulously structured to provide a balanced blend of theoretical knowledge and practical application across eight semesters. The curriculum includes core courses, departmental electives, science electives, and laboratory sessions designed to build foundational skills and advanced competencies.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | CS102 | Physics for Computer Science | 3-1-0-4 | None |
| 1 | CS103 | Introduction to Programming | 3-0-2-5 | None |
| 1 | CS104 | Engineering Graphics | 2-0-2-4 | None |
| 1 | CS105 | English for Engineers | 3-0-0-3 | None |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Chemistry for Computer Science | 3-1-0-4 | None |
| 2 | CS203 | Data Structures and Algorithms | 3-0-2-5 | CS103 |
| 2 | CS204 | Computer Organization and Architecture | 3-0-2-5 | None |
| 2 | CS205 | Object-Oriented Programming with Java | 3-0-2-5 | CS103 |
| 3 | CS301 | Database Management Systems | 3-0-2-5 | CS203 |
| 3 | CS302 | Operating Systems | 3-0-2-5 | CS204 |
| 3 | CS303 | Computer Networks | 3-0-2-5 | CS204 |
| 3 | CS304 | Software Engineering | 3-0-2-5 | CS203 |
| 3 | CS305 | Probability and Statistics | 3-1-0-4 | CS101 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-0-2-5 | CS203 |
| 4 | CS402 | Distributed Systems | 3-0-2-5 | CS303 |
| 4 | CS403 | Web Technologies | 3-0-2-5 | CS205 |
| 4 | CS404 | Compiler Design | 3-0-2-5 | CS301 |
| 4 | CS405 | Artificial Intelligence Fundamentals | 3-0-2-5 | CS305 |
| 5 | CS501 | Machine Learning | 3-0-2-5 | CS405 |
| 5 | CS502 | Cybersecurity Principles | 3-0-2-5 | CS303 |
| 5 | CS503 | Data Mining and Big Data Analytics | 3-0-2-5 | CS301 |
| 5 | CS504 | Cloud Computing | 3-0-2-5 | CS303 |
| 5 | CS505 | Human-Computer Interaction | 3-0-2-5 | CS403 |
| 6 | CS601 | Advanced Machine Learning | 3-0-2-5 | CS501 |
| 6 | CS602 | Network Security | 3-0-2-5 | CS502 |
| 6 | CS603 | Database Systems Design | 3-0-2-5 | CS301 |
| 6 | CS604 | Software Architecture | 3-0-2-5 | CS304 |
| 6 | CS605 | Capstone Project I | 0-0-6-10 | CS501, CS502 |
| 7 | CS701 | Research Methodology | 3-0-0-3 | None |
| 7 | CS702 | Capstone Project II | 0-0-6-10 | CS605 |
| 7 | CS703 | Thesis Writing and Presentation | 2-0-0-2 | CS701 |
| 8 | CS801 | Industry Internship | 0-0-0-20 | CS605, CS702 |
Advanced Departmental Elective Courses
Departmental electives offer students the opportunity to specialize in niche areas that align with their career goals and personal interests. Here are detailed descriptions of several advanced courses:
- Deep Learning with TensorFlow: This course provides an in-depth exploration of neural network architectures, convolutional networks, recurrent networks, and transformers. Students will learn to implement models using TensorFlow and PyTorch frameworks, applying them to image recognition, natural language processing, and time series prediction tasks.
- Blockchain and Cryptocurrency Technologies: This course covers the fundamentals of blockchain technology, cryptographic protocols, smart contracts, and decentralized applications (dApps). It includes hands-on development using Ethereum and Hyperledger platforms, preparing students for careers in fintech, supply chain, and digital identity sectors.
- Mobile Application Development: Students explore modern mobile app development frameworks such as Flutter and React Native. The course emphasizes cross-platform development strategies, user interface design principles, and integration with backend services using Firebase and REST APIs.
- Computer Vision and Image Processing: This course introduces students to image processing techniques, feature extraction methods, object detection algorithms, and deep learning applications in computer vision. Practical sessions involve using OpenCV, MATLAB, and Python-based libraries to solve real-world problems in medical imaging, autonomous vehicles, and surveillance systems.
- Quantum Computing Fundamentals: An introductory course to quantum mechanics as applied to computing. Students will understand qubits, superposition, entanglement, and quantum algorithms. The course includes simulations using Qiskit and Cirq platforms, preparing students for future research in quantum technologies.
- Natural Language Processing (NLP): This course delves into language models, sentiment analysis, named entity recognition, and text generation techniques. Students will work with datasets from Kaggle and Hugging Face, implementing transformer-based models like BERT, RoBERTa, and GPT for practical NLP tasks.
- Embedded Systems Design: A comprehensive study of microcontroller architecture, real-time operating systems, embedded C programming, and hardware-software co-design. Students will design and prototype IoT devices using ARM Cortex-M series microcontrollers and Arduino platforms.
- DevOps and CI/CD Pipelines: This course covers automation tools like Jenkins, Docker, Kubernetes, GitLab CI, and AWS CodePipeline. Students learn to implement continuous integration and deployment strategies, enabling rapid and reliable software delivery in enterprise environments.
- Reinforcement Learning: Focused on sequential decision-making problems, this course teaches Markov Decision Processes (MDPs), Q-learning, policy gradients, and actor-critic methods. Practical applications include game AI, robotics control, and autonomous systems using OpenAI Gym and Stable Baselines3.
- Big Data Analytics Using Hadoop: This course introduces students to distributed computing paradigms and big data frameworks like Apache Spark, Hive, Pig, and Kafka. Students will perform large-scale data processing tasks, visualize insights using Tableau and Power BI, and develop scalable analytics solutions for enterprise applications.
Project-Based Learning Philosophy
The department believes in project-based learning as a cornerstone of academic excellence. Mini-projects are assigned during the second and third years to reinforce core concepts learned in class. These projects involve real-world scenarios and encourage teamwork, innovation, and problem-solving skills.
The final-year thesis or capstone project is a significant component of the program. Students work closely with faculty mentors to select a topic relevant to current industry trends or emerging research areas. Projects are evaluated based on technical depth, originality, documentation quality, presentation skills, and demonstration of practical impact.
Students can choose from a list of predefined projects provided by faculty members or propose their own ideas. The selection process involves proposal submission, mentor assignment, progress tracking, and final defense presentations. The department provides resources including research grants, access to software licenses, and collaboration opportunities with industry partners.