Curriculum Overview
The Computer Science curriculum at Netaji Subhas University Jamshedpur is designed to provide students with a strong foundation in core computing concepts while allowing flexibility for specialization. The program spans eight semesters, each building upon previous knowledge and introducing new skills relevant to modern technology.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Engineering Mathematics I | 4-0-0-4 | - |
| I | CS103 | Physics for Engineers | 3-0-0-3 | - |
| I | CS104 | Chemistry for Engineers | 3-0-0-3 | - |
| I | CS105 | English for Communication | 2-0-0-2 | - |
| I | CS106 | Lab: Introduction to Programming | 0-0-3-1 | - |
| II | CS201 | Data Structures & Algorithms | 3-0-0-3 | CS101 |
| II | CS202 | Engineering Mathematics II | 4-0-0-4 | CS102 |
| II | CS203 | Computer Organization & Architecture | 3-0-0-3 | - |
| II | CS204 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| II | CS205 | Discrete Mathematics | 3-0-0-3 | CS102 |
| II | CS206 | Lab: Data Structures & Algorithms | 0-0-3-1 | - |
| III | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| III | CS302 | Operating Systems | 3-0-0-3 | CS203 |
| III | CS303 | Computer Networks | 3-0-0-3 | CS203 |
| III | CS304 | Software Engineering | 3-0-0-3 | CS204 |
| III | CS305 | Probability & Statistics | 3-0-0-3 | CS102 |
| III | CS306 | Lab: Software Engineering | 0-0-3-1 | - |
| IV | CS401 | Design & Analysis of Algorithms | 3-0-0-3 | CS201 |
| IV | CS402 | Artificial Intelligence | 3-0-0-3 | CS301 |
| IV | CS403 | Cryptography & Network Security | 3-0-0-3 | CS303 |
| IV | CS404 | Human-Computer Interaction | 3-0-0-3 | - |
| IV | CS405 | Compiler Design | 3-0-0-3 | CS201 |
| IV | CS406 | Lab: Artificial Intelligence | 0-0-3-1 | - |
| V | CS501 | Machine Learning | 3-0-0-3 | CS301 |
| V | CS502 | Data Mining & Warehousing | 3-0-0-3 | CS301 |
| V | CS503 | Embedded Systems | 3-0-0-3 | CS203 |
| V | CS504 | Mobile Computing | 3-0-0-3 | - |
| V | CS505 | Computer Graphics | 3-0-0-3 | CS201 |
| V | CS506 | Lab: Machine Learning | 0-0-3-1 | - |
| VI | CS601 | Advanced Computer Networks | 3-0-0-3 | CS303 |
| VI | CS602 | Distributed Systems | 3-0-0-3 | CS401 |
| VI | CS603 | Cloud Computing | 3-0-0-3 | - |
| VI | CS604 | Robotics & Automation | 3-0-0-3 | - |
| VI | CS605 | Natural Language Processing | 3-0-0-3 | CS402 |
| VI | CS606 | Lab: Robotics & Automation | 0-0-3-1 | - |
| VII | CS701 | Research Methodology | 2-0-0-2 | - |
| VII | CS702 | Capstone Project I | 3-0-0-3 | - |
| VII | CS703 | Elective I | 3-0-0-3 | - |
| VII | CS704 | Elective II | 3-0-0-3 | - |
| VII | CS705 | Internship | 0-0-0-6 | - |
| VIII | CS801 | Capstone Project II | 3-0-0-3 | - |
| VIII | CS802 | Elective III | 3-0-0-3 | - |
| VIII | CS803 | Elective IV | 3-0-0-3 | - |
| VIII | CS804 | Final Project Presentation | 0-0-0-2 | - |
Detailed Elective Course Descriptions
Machine Learning: This course introduces students to fundamental algorithms in supervised and unsupervised learning, including regression, classification, clustering, decision trees, neural networks, and reinforcement learning. Students will learn to implement these techniques using Python libraries like scikit-learn, TensorFlow, and PyTorch.
Data Mining & Warehousing: This elective covers data preprocessing, association rule mining, classification algorithms, cluster analysis, anomaly detection, and data warehouse design principles. It includes hands-on experience with tools like Apache Spark, Weka, and Tableau.
Embedded Systems: Students explore microcontroller programming, real-time operating systems, hardware-software co-design, and embedded software development environments. The course includes practical projects involving Arduino, Raspberry Pi, and ARM Cortex-M processors.
Mobile Computing: This course focuses on mobile application development for Android and iOS platforms, wireless communication protocols, location-based services, and mobile security challenges. Students will build cross-platform applications using Flutter or React Native frameworks.
Computer Graphics: Students study 2D and 3D graphics rendering pipelines, geometric transformations, lighting models, texture mapping, and animation techniques. Practical sessions involve developing interactive graphics applications using OpenGL and WebGL.
Natural Language Processing: This course delves into text preprocessing, sentiment analysis, named entity recognition, machine translation, and dialogue systems. Students will use NLP libraries like NLTK, spaCy, and Hugging Face Transformers for practical assignments.
Distributed Systems: The course covers distributed computing architectures, consensus protocols, fault tolerance, load balancing, and scalability patterns. Students will implement distributed applications using Java, Go, or Python.
Cloud Computing: This elective explores cloud architecture models, virtualization technologies, containerization with Docker and Kubernetes, and cloud service providers like AWS, Azure, and Google Cloud Platform. Students will deploy scalable applications in cloud environments.
Robotics & Automation: Students learn robot kinematics, control systems, sensor integration, path planning, and autonomous navigation. Projects involve building and programming robots using ROS (Robot Operating System) and Python-based frameworks.
Advanced Computer Networks: This course examines advanced networking concepts including QoS, network security, wireless networks, SDN (Software Defined Networking), and 5G technologies. Students will analyze and simulate complex network topologies using NS-3 and Wireshark.
Project-Based Learning Philosophy
Our department believes in experiential learning through project-based education. The curriculum incorporates mini-projects throughout the program to reinforce theoretical concepts and develop practical skills.
Mini-projects are assigned during the second, third, and fourth semesters. These projects typically last 6–8 weeks and involve solving real-world problems under faculty supervision. Each project must be documented with a final report, presentation, and demonstration of working code or prototype.
The final-year thesis/capstone project is a comprehensive endeavor spanning the entire eighth semester. Students select a research topic in consultation with faculty mentors and work on an original contribution to the field. The project includes literature review, methodology development, implementation, testing, and documentation.
Project selection occurs through a process involving student preferences, faculty availability, industry partnerships, and research interests. Faculty members guide students based on their expertise and past contributions to relevant areas.