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B-TECH in Computer Science And Engineering at National Institute of Technology Raipur
Raipur, Chhattisgarh
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About the Specialization
What is Computer Science and Engineering at National Institute of Technology Raipur Raipur?
This Computer Science and Engineering program at National Institute of Technology Raipur focuses on foundational computing principles, advanced algorithms, and emerging technologies crucial for India''''s digital transformation. It integrates theoretical knowledge with practical skills, preparing students for roles in software development, data science, AI, and cybersecurity, critical sectors driving the Indian economy''''s growth. The curriculum emphasizes problem-solving and innovation.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, aspiring to build a career in technology. It caters to individuals seeking entry-level software development, data analytics, or AI engineering positions in leading Indian tech companies and startups, as well as those aiming for higher studies or entrepreneurship in the rapidly evolving Indian tech landscape.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Software Engineer, Data Scientist, Machine Learning Engineer, and Cybersecurity Analyst. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more. The strong curriculum aligns with industry demand, fostering growth trajectories in IT services, product development, and research within Indian and global MNCs operating in India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time in Semesters 1-2 to build a strong foundation in C/C++ or Python. Practice coding daily on platforms like HackerRank, LeetCode (easy level), and GeeksforGeeks to enhance logical thinking and problem-solving skills. Focus on understanding data types, control flow, functions, and basic data structures.
Tools & Resources
HackerRank, LeetCode (easy), GeeksforGeeks, NPTEL courses on Programming
Career Connection
A robust programming foundation is crucial for all entry-level software development roles and competitive coding rounds during placements.
Excel in Core Mathematics- (Semester 1-2)
Focus intently on Discrete Mathematics, Calculus, and Linear Algebra. These subjects form the backbone of advanced CSE topics like algorithms, machine learning, and data science. Join study groups and solve extra problems from textbooks and online resources to strengthen conceptual clarity.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL courses on Mathematics, Peer study groups
Career Connection
Strong mathematical aptitude is highly valued for roles in AI/ML, data science, and research, demonstrating analytical problem-solving capabilities.
Engage in Technical Clubs and Societies- (Semester 1-2)
Participate actively in NIT Raipur''''s technical clubs, such as the Computer Science Society or coding clubs. This fosters peer learning, exposes you to project-based work, and helps develop soft skills. Begin with small projects and group activities to apply theoretical knowledge practically.
Tools & Resources
Institution''''s technical clubs, Departmental workshops, Intra-college hackathons
Career Connection
Early involvement builds a strong network, develops teamwork, and provides initial project experience vital for resumes and interviews.
Intermediate Stage
Build a Strong Data Structures & Algorithms (DSA) Profile- (Semester 3-5)
Intensively practice DSA from Semester 3 onwards. Aim to solve medium-to-hard problems on platforms like LeetCode and CodeChef. Participate in competitive programming contests to improve speed, accuracy, and problem-solving strategies. Master various data structures and algorithm paradigms.
Tools & Resources
LeetCode, CodeChef, Codeforces, NPTEL/Coursera DSA courses
Career Connection
Exceptional DSA skills are a primary filter for top tech companies (product-based) during placement interviews for software engineering roles.
Undertake Mini-Projects and Internships- (Semester 4-6)
Apply classroom knowledge by building practical mini-projects in areas like web development, app development, or basic AI/ML. Seek out short-term internships (e.g., summer internships, remote projects) to gain industry exposure and understand real-world software development cycles.
Tools & Resources
GitHub, LinkedIn for internships, Company websites for opportunities, Project-based learning platforms
Career Connection
Practical projects and internships provide invaluable experience, demonstrate applied skills, and significantly boost your resume for placements.
Explore Specializations and Emerging Technologies- (Semester 4-6)
Utilize elective courses and online resources to dive deeper into areas of interest like Machine Learning, Data Science, Cyber Security, or Cloud Computing. Start learning new programming languages or frameworks relevant to your chosen specialization, such as Python for ML or JavaScript for web development.
Tools & Resources
Coursera, edX, Udemy, Specialized blogs and tutorials, Official documentation of frameworks
Career Connection
Early specialization helps in identifying career paths and acquiring targeted skills, making you a more attractive candidate for specific industry roles.
Advanced Stage
Engage in Research or Capstone Projects- (Semester 7-8)
Work on a significant major project (Capstone Project) or a research paper under faculty mentorship. This involves in-depth problem solving, system design, implementation, and documentation. Focus on innovative solutions and rigorous evaluation, which could lead to publications or patent applications.
Tools & Resources
Research labs within NIT Raipur, Faculty mentorship, IEEE Xplore, ACM Digital Library, GitHub
Career Connection
Major projects showcase advanced technical abilities, research acumen, and leadership, highly beneficial for higher studies (M.Tech/Ph.D) or R&D roles in industry.
Intensive Placement Preparation- (Semester 7-8)
During the final year, dedicate substantial time to placement preparation. This includes mock interviews (technical and HR), aptitude test practice, resume building, and practicing coding challenges under timed conditions. Attend workshops organized by the training and placement cell and alumni sessions.
Tools & Resources
T&P Cell resources, Mock interview platforms, Aptitude test books/websites, LinkedIn for professional networking
Career Connection
Focused preparation ensures readiness for campus placements, maximizing chances of securing a desirable job offer in a top company.
Network and Seek Mentorship- (Semester 6-8)
Actively network with alumni, industry professionals, and faculty members. Attend industry seminars, conferences, and career fairs (online and offline). Seek mentorship to gain insights into industry trends, career opportunities, and personal development strategies.
Tools & Resources
LinkedIn, Alumni association events, Industry conferences (e.g., Nasscom events), Departmental guest lectures
Career Connection
Networking opens doors to hidden opportunities, provides career guidance, and can lead to referrals for jobs or further studies.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Chemistry, and Mathematics, and a valid JEE Main score for admission through JoSAA/CSAB counselling.
Duration: 8 semesters / 4 years
Credits: 169 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CH101 | Applied Chemistry | Core | 4 | Atomic Structure and Chemical Bonding, Stereochemistry and Reaction Mechanisms, Spectroscopy and Analytical Techniques, Electrochemistry and Corrosion, Polymer Chemistry, Water Technology |
| MA101 | Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Multivariable Calculus, Vector Calculus, Matrix Algebra |
| EE101 | Basic Electrical Engineering | Core | 4 | DC Circuits and Network Theorems, AC Circuits and Phasor Analysis, Transformers and Electrical Machines, Power Systems Basics, Measuring Instruments, Electrical Safety and Earthing |
| EE102 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, AC Circuit Measurements, Characteristics of Electrical Machines, Power Factor Improvement, Domestic Wiring, CRO and Function Generator Usage |
| CS101 | Computer Programming | Core | 4 | Programming Fundamentals (C Language), Data Types and Operators, Control Structures (Loops, Conditionals), Functions and Arrays, Pointers and Structures, File Handling |
| CS102 | Computer Programming Lab | Lab | 1 | C Program Development, Debugging Techniques, Array and String Manipulations, Function Implementations, Pointer Operations, Problem Solving through Programming |
| HS101 | Professional Communication | Core | 3 | Fundamentals of Communication, Verbal and Non-verbal Communication, Written Communication Skills, Listening and Speaking Skills, Group Discussions and Presentations, Interpersonal Communication |
| ME101 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Introduction to AutoCAD, Dimensioning and Tolerancing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PH101 | Applied Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Wave Optics and Lasers, Electromagnetism, Semiconductor Devices, Fiber Optics |
| PH102 | Applied Physics Lab | Lab | 1 | Optical Experiments, Semiconductor Device Characteristics, Magnetic Field Measurements, Diffraction and Interference, Laser Characteristics, Acoustic Measurements |
| MA102 | Mathematics - II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Fourier Series and Transforms, Partial Differential Equations, Complex Analysis, Probability Distributions |
| ME102 | Engineering Mechanics | Core | 3 | Statics of Particles and Rigid Bodies, Equilibrium and Friction, Kinematics of Particles and Rigid Bodies, Kinetics of Particles (Newton''''s Laws), Work-Energy Principle, Impulse-Momentum Principle |
| ME103 | Manufacturing Process | Core | 3 | Metal Casting Processes, Forming and Fabrication Processes, Machining Processes (Lathe, Milling), Welding and Joining, Sheet Metal Operations, Introduction to CAD/CAM |
| ME104 | Manufacturing Process Lab | Lab | 1 | Workshop Practices (Fitting, Carpentry), Machining Operations Practice, Welding Techniques, Foundry Practice, Sheet Metal Work, Measurement using Precision Instruments |
| BT101 | Environmental Science | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution (Air, Water, Soil), Natural Resources and Conservation, Climate Change and Global Warming, Waste Management, Environmental Policies and Laws |
| HS102 | Constitution of India | Core | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Governments, Judiciary and Local Governance, Constitutional Amendments, Emergency Provisions |
| PE101 | Sports & Yoga | Audit | 0 | Physical Fitness Principles, Yoga Asanas and Pranayama, Basic Sports Skills (Team Sports), Sportsmanship and Teamwork, Health and Wellness, Stress Management Techniques |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Discrete Mathematics | Core | 4 | Mathematical Logic and Proof Techniques, Set Theory, Relations, and Functions, Combinatorics and Counting Principles, Graph Theory (Trees, Connectivity), Algebraic Structures, Recurrence Relations |
| CS201 | Data Structures and Algorithms | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees and Binary Search Trees, Graphs and Graph Traversal Algorithms, Hashing Techniques, Sorting Algorithms, Searching Algorithms |
| CS202 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linear Data Structures, Implementation of Non-linear Data Structures, Algorithm Efficiency Analysis, Recursive Algorithms, Graph Algorithm Implementations, Problem Solving with Data Structures |
| CS203 | Object Oriented Programming | Core | 4 | Object-Oriented Concepts (Classes, Objects), Inheritance and Polymorphism, Encapsulation and Abstraction, Constructors and Destructors, Exception Handling, Introduction to Java/C++ |
| CS204 | Object Oriented Programming Lab | Lab | 1 | C++ / Java Program Development, Class and Object Implementation, Inheritance and Polymorphism Examples, Exception Handling in OOP, File I/O in OOP, GUI Development Basics |
| EC201 | Digital Logic Design | Core | 3 | Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits (Flip-flops), Registers and Counters, Memory Organization, HDL (Hardware Description Language) Basics |
| EC202 | Digital Logic Design Lab | Lab | 1 | Verification of Logic Gates, Implementation of Combinational Circuits, Implementation of Sequential Circuits, Design of Registers and Counters, Use of Digital ICs, Truth Table Verification |
| HS201 | Managerial Economics and Financial Accounting | Core | 3 | Demand and Supply Analysis, Production and Cost Analysis, Market Structures and Pricing Decisions, Introduction to Financial Accounting, Financial Statements Analysis, Capital Budgeting |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA202 | Mathematics - III (Probability & Statistics) | Core | 4 | Probability Theory and Random Variables, Discrete and Continuous Probability Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression Analysis, Stochastic Processes |
| CS205 | Operating System | Core | 4 | Operating System Structures, Process Management and CPU Scheduling, Memory Management (Paging, Segmentation), File Systems and I/O Management, Concurrency Control and Deadlocks, Distributed Operating Systems |
| CS206 | Operating System Lab | Lab | 1 | Linux Commands and Shell Scripting, Process Management System Calls, CPU Scheduling Algorithms Implementation, Memory Allocation Algorithms, Inter-process Communication, Synchronization Problems (e.g., Dining Philosophers) |
| CS207 | Database Management Systems | Core | 4 | Database System Architecture, ER Model and Relational Model, SQL Query Language, Normalization (1NF, 2NF, 3NF, BCNF), Transaction Management and Concurrency Control, Database Security and Recovery |
| CS208 | Database Management Systems Lab | Lab | 1 | SQL DDL and DML Commands, Complex SQL Queries and Joins, PL/SQL Programming, Database Design and Implementation, JDBC/ODBC Connectivity, Trigger and Stored Procedures |
| CS209 | Computer Organization and Architecture | Core | 4 | Basic Computer Organization, Processor Design and Instruction Set Architecture, Memory Hierarchy (Cache, Virtual Memory), Input/Output Organization, Pipelining and Parallel Processing, Control Unit Design |
| CS210 | Software Engineering | Core | 3 | Software Development Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Techniques, Software Maintenance and Evolution, Software Project Management Concepts |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis (Time/Space Complexity), Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Graph Algorithms (Shortest Path, MST), NP-Completeness and Approximation Algorithms |
| CS302 | Design and Analysis of Algorithms Lab | Lab | 1 | Implementation of Sorting/Searching Algorithms, Implementation of Graph Algorithms, Dynamic Programming Problems, Analysis of Algorithm Performance, Recursion and Backtracking, Hashing Techniques |
| CS303 | Theory of Computation | Core | 3 | Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines, Undecidability, Chomsky Hierarchy, P and NP Classes |
| CS304 | Compiler Design | Core | 3 | Phases of a Compiler, Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization and Code Generation |
| CS305 | Compiler Design Lab | Lab | 1 | Implementation of Lexical Analyzer, Implementation of Parsers (LL, LR), Symbol Table Management, Intermediate Code Generation, Error Handling, Simple Compiler Development |
| CS306 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Data Link Layer (Error, Flow Control), Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS), Network Security Basics |
| CS307 | Computer Networks Lab | Lab | 1 | Socket Programming, Network Configuration and Troubleshooting, Packet Sniffing and Analysis (Wireshark), Routing Protocols Implementation, Client-Server Application Development, Network Security Tools |
| ID301 | Disaster Management | Core | 2 | Types of Disasters and Hazards, Disaster Risk Reduction, Preparedness and Mitigation Strategies, Response and Recovery, Role of Government and NGOs, Case Studies of Disasters in India |
| CS328 | Cloud Computing | Open Elective | 3 | Cloud Computing Paradigms (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Deployment Models, Cloud Storage and Networking, Cloud Security Challenges, Major Cloud Providers (AWS, Azure, GCP) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS320 | Data Analytics | Professional Elective | 3 | Introduction to Big Data, Data Collection and Preprocessing, Statistical Methods for Data Analysis, Machine Learning for Analytics, Data Visualization Techniques, Big Data Technologies (Hadoop, Spark) |
| CS321 | Artificial Intelligence | Professional Elective | 3 | AI Agents and Intelligent Systems, Search Algorithms (DFS, BFS, A*), Knowledge Representation and Reasoning, Machine Learning Fundamentals, Natural Language Processing Basics, Expert Systems and Robotics |
| CS308 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning Basics, Neural Networks and Deep Learning Introduction, Model Evaluation and Hyperparameter Tuning, Feature Engineering |
| CS309 | Machine Learning Lab | Lab | 1 | Python for Machine Learning (Scikit-learn), Data Preprocessing and Visualization, Implementation of Regression Models, Implementation of Classification Models, Clustering Algorithms Practice, Neural Network Implementation (TensorFlow/PyTorch) |
| CS310 | Web Technologies | Core | 3 | HTML5 and CSS3, JavaScript and DOM Manipulation, Client-Server Architecture, Web Frameworks (e.g., Node.js, Django, Flask), RESTful APIs, Web Security Fundamentals |
| CS311 | Web Technologies Lab | Lab | 1 | Frontend Development (HTML, CSS, JS), Backend Development with Frameworks, Database Integration for Web Apps, API Development and Consumption, Web Application Deployment, Responsive Web Design |
| CS312 | Minor Project – I | Project | 2 | Problem Identification and Literature Review, System Design and Architecture, Implementation and Coding, Testing and Debugging, Project Documentation, Presentation and Demonstration |
| CS313 | Industrial Training – I | Industrial Training | 2 | Industry Exposure and Practices, Application of Academic Knowledge, Professional Ethics and Conduct, Teamwork and Collaboration, Report Writing on Industrial Experience, Presentation of Learning Outcomes |
| CS329 | Cyber Security | Open Elective | 3 | Introduction to Cyber Security, Cryptography and Network Security, Web and Application Security, Malware Analysis and Countermeasures, Ethical Hacking and Penetration Testing, Security Policies and Incident Response |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS420 | Deep Learning | Professional Elective | 3 | Introduction to Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformer Networks, Deep Learning Frameworks (TensorFlow, PyTorch) |
| CS421 | Distributed Systems | Professional Elective | 3 | Introduction to Distributed Systems, Client-Server and Peer-to-Peer Architectures, Remote Procedure Call (RPC), Distributed Consensus (Paxos, Raft), Fault Tolerance and Replication, Distributed File Systems |
| CS401 | Software Project Management | Core | 3 | Software Project Planning and Estimation, Risk Management in Software Projects, Software Quality Management, Project Scheduling and Tracking, Agile Methodologies (Scrum, Kanban), Configuration Management |
| CS402 | High Performance Computing | Core | 3 | Parallel Computing Architectures, Shared Memory Programming (OpenMP), Distributed Memory Programming (MPI), GPU Computing (CUDA), Performance Metrics and Optimization, Cluster and Grid Computing |
| CS403 | High Performance Computing Lab | Lab | 1 | OpenMP Programming, MPI Programming, CUDA Kernel Development, Parallel Algorithm Implementation, Performance Profiling and Tuning, Vectorization Techniques |
| CS428 | Internet of Things | Open Elective | 3 | IoT Architecture and Paradigms, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols (MQTT, CoAP), IoT Platforms (AWS IoT, Azure IoT Hub), Data Analytics for IoT, IoT Security and Privacy |
| CS404 | Minor Project – II | Project | 3 | Advanced Problem Definition and Scope, System Design and Implementation, Thorough Testing and Validation, Project Report Writing with Technical Depth, Presentation of Project Outcomes, Application of Advanced Concepts |
| CS405 | Industrial Training – II / Internship | Industrial Training | 2 | Real-world Project Experience, Mentorship and Professional Growth, Advanced Technical Skill Application, Corporate Culture and Work Ethics, Problem Solving in Industry Settings, Networking and Career Planning |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS429 | Reinforcement Learning | Professional Elective | 3 | Introduction to Reinforcement Learning, Markov Decision Processes, Dynamic Programming (Value Iteration, Policy Iteration), Monte Carlo Methods, Temporal-Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning |
| CS430 | Image Processing | Professional Elective | 3 | Digital Image Fundamentals, Image Enhancement (Spatial and Frequency Domain), Image Restoration, Image Segmentation, Feature Extraction and Representation, Object Recognition |
| CS406 | Major Project | Project | 10 | Comprehensive Problem Formulation, Research and Development, System Design, Implementation, and Testing, Performance Evaluation and Optimization, Detailed Project Report and Documentation, Public Presentation and Viva Voce |
| HS403 | Organizational Behavior | Humanities and Social Science Elective | 3 | Foundations of Organizational Behavior, Individual Behavior (Personality, Perception), Group Dynamics and Teamwork, Leadership and Motivation, Conflict and Negotiation, Organizational Culture and Change |
