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B-TECH in Computer Science And Engineering at National Institute of Technology Patna
Patna, Bihar
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About the Specialization
What is Computer Science and Engineering at National Institute of Technology Patna Patna?
This Computer Science and Engineering program at National Institute of Technology Patna focuses on equipping students with a robust foundation in computing principles and their advanced applications. It integrates theoretical knowledge with practical skills, preparing graduates for the dynamic Indian IT industry. The curriculum is designed to foster innovation and critical thinking, catering to the evolving demands of technology sectors both domestically and globally. The program emphasizes problem-solving and system development.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics, logical reasoning, and a keen interest in technology and problem-solving. It suits aspiring software developers, data scientists, network engineers, and researchers. Students passionate about artificial intelligence, cybersecurity, or cloud computing will find the curriculum engaging. It also caters to those seeking a strong theoretical base for higher studies or entrepreneurship in the tech domain.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as Software Engineers, Data Analysts, AI/ML Engineers, Cybersecurity Specialists, and Cloud Architects. Entry-level salaries typically range from INR 5-10 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry certifications, enhancing career growth. Alumni find opportunities in top IT firms, startups, and public sector organizations across India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate consistent time to practice programming concepts learned in CS 101/102. Focus on clear logic, debugging, and efficient code. Solve problems daily to build a strong base for advanced subjects.
Tools & Resources
GeeksforGeeks, HackerRank, CodeChef, NPTEL videos on C programming
Career Connection
A strong coding foundation is critical for internships and placements in software development roles. It enhances problem-solving skills valued by all tech companies.
Build a Strong Mathematical and Scientific Base- (Semester 1-2)
Pay close attention to Engineering Mathematics and Physics/Chemistry. These foundational subjects develop analytical thinking, which is crucial for understanding complex algorithms and system design later on.
Tools & Resources
Khan Academy, MIT OpenCourseware, Reference textbooks for clear concepts
Career Connection
Solid mathematical skills are essential for areas like Data Science, Machine Learning, and Algorithm Design, paving the way for specialized tech careers.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups, discuss challenging topics, and collaborate on small academic projects. This improves understanding, communication, and teamwork skills.
Tools & Resources
WhatsApp groups for study, GitHub for collaborative coding, college hackathon clubs
Career Connection
Teamwork and collaboration are highly sought-after skills in the corporate world, preparing you for effective work in professional software development teams.
Intermediate Stage
Apply DSA and OOP Concepts to Real-world Problems- (Semester 3-5)
Beyond theoretical knowledge, actively implement data structures and algorithms, and apply OOP principles to build small applications. Participate in competitive programming contests regularly.
Tools & Resources
LeetCode, TopCoder, Kaggle for datasets, GitHub for personal projects
Career Connection
Mastering DSA and OOP is a primary filter for most tech interviews for product companies and top-tier service companies, leading to better placement opportunities.
Seek Early Industry Exposure through Internships/Workshops- (Semester 3-5)
Look for summer internships or join workshops related to emerging technologies like AI/ML, Cloud, or Web Development. This provides practical insights and helps identify career interests.
Tools & Resources
Internshala, LinkedIn, College placement cell notices, Online certification platforms
Career Connection
Early exposure helps build a professional network, understand industry workflows, and makes you more attractive to recruiters for future full-time roles and final year internships.
Develop Specialization Skills and Portfolio- (Semester 3-5)
Identify areas of interest (e.g., web development, data science, cybersecurity) and start building a portfolio of projects. Contribute to open-source projects or create your own applications.
Tools & Resources
Udemy, Coursera for specialized courses, GitHub for version control, Personal blog/portfolio website
Career Connection
A strong project portfolio showcases practical skills and passion, distinguishing you during placements and helping you secure roles in your chosen specialization.
Advanced Stage
Focus on Major Project and Research- (Semester 6-8)
Invest deeply in your major project (CS 403, CS 451), choosing a topic that aligns with your career goals. Aim for innovative solutions, publish papers if possible, or build a production-ready system.
Tools & Resources
Research papers (IEEE, ACM), Advising faculty, Cloud platforms for deployment (AWS, GCP)
Career Connection
A significant final year project is a powerful resume booster and a strong talking point in interviews, demonstrating problem-solving and execution capabilities.
Intensive Placement and Interview Preparation- (Semester 6-8)
Practice mock interviews (technical and HR), participate in campus recruitment drives, and refine your resume and soft skills. Focus on company-specific preparation and aptitude tests.
Tools & Resources
Placement cell resources, Mock interview platforms, Aptitude test preparation books/sites
Career Connection
This direct preparation translates into successful conversion of interview opportunities into job offers, securing your career launch.
Network Professionally and Mentor Juniors- (Semester 6-8)
Connect with alumni, industry professionals, and faculty. Attend industry seminars and conferences. Mentor junior students to reinforce your understanding and develop leadership qualities.
Tools & Resources
LinkedIn, Professional communities (e.g., local ACM chapters), College alumni network
Career Connection
Professional networking opens doors to unexpected opportunities, mentorship, and deeper industry insights, invaluable for long-term career growth and leadership roles.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 4 years (8 semesters)
Credits: 168 Credits
Assessment: Internal: 40% (Theory), 60% (Practical), External: 60% (Theory), 40% (Practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 101 | Engineering Mathematics-I | Core | 4 | Matrices, Differential Calculus, Integral Calculus, Ordinary Differential Equations, Multivariable Calculus |
| PH 101 | Engineering Physics | Core | 4 | Mechanics, Electromagnetism, Quantum Physics, Optics, Solid State Physics |
| CY 101 | Engineering Chemistry | Core | 4 | Atomic Structure & Bonding, Thermodynamics, Electrochemistry, Spectroscopy, Organic Chemistry |
| CS 101 | Programming for Problem Solving | Core | 3 | Algorithms and Flowcharts, C Language Fundamentals, Control Statements, Functions and Arrays, Pointers and Structures |
| PH 102 | Engineering Physics Lab | Lab | 1 | Measurement and Error Analysis, Oscillations and Waves, Light Interference and Diffraction, PN Junction Diode Characteristics, Hall Effect Determination |
| CY 102 | Engineering Chemistry Lab | Lab | 1 | Water Analysis Techniques, Chemical Synthesis Methods, pH Metry and Potentiometry, Conductometry Experiments, Calorimetry and Reaction Rates |
| CS 102 | Programming for Problem Solving Lab | Lab | 1 | C Programming Exercises, Conditional and Loop Structures, Function Implementation, Array and String Operations, Problem Solving with Pointers |
| HS 101 | English for Communication | Humanities | 2 | Grammar and Vocabulary, Reading Comprehension Strategies, Formal Writing Skills, Oral Communication Practice, Public Speaking and Presentations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 102 | Engineering Mathematics-II | Core | 4 | Vector Calculus, Complex Analysis, Laplace Transform, Fourier Series, Partial Differential Equations |
| EE 101 | Basic Electrical Engineering | Core | 4 | DC Circuit Analysis, AC Circuit Fundamentals, Transformers, DC Machines, AC Machines and Power Systems |
| ME 101 | Engineering Graphics & Design | Core | 2 | Orthographic Projections, Isometric Views, Sectional Views, AutoCAD Basics, Assembly Drawing |
| EC 101 | Basic Electronics Engineering | Core | 3 | Semiconductor Diodes, Bipolar Junction Transistors, Rectifiers and Filters, Amplifiers and Oscillators, Introduction to Digital Logic |
| EE 102 | Basic Electrical Engineering Lab | Lab | 1 | Ohm''''s Law and Kirchhoff''''s Laws, Circuit Theorems Verification, AC Circuit Measurements, Transformer Characteristics, DC Motor Speed Control |
| ME 102 | Workshop/Manufacturing Practices | Lab | 2 | Fitting Shop, Carpentry Shop, Welding Shop, Machining Processes, Sheet Metal Work, Foundry Practices |
| EC 102 | Basic Electronics Engineering Lab | Lab | 1 | Diode and Zener Diode Characteristics, Rectifier Circuits, Transistor Amplifier Design, Oscillator Circuits, Basic Logic Gate Implementation |
| EV 101 | Environmental Science | Humanities | 2 | Ecosystems and Biogeochemical Cycles, Environmental Pollution Control, Natural Resources Management, Biodiversity and Conservation, Environmental Impact Assessment |
| HS 102 | Professional Communication | Humanities | 2 | Business Communication Etiquette, Technical Report Writing, Presentation Skills Development, Group Discussion Strategies, Interview Techniques and Resume Building |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS 201 | Data Structures and Algorithms | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graph Algorithms, Sorting and Searching Techniques |
| CS 202 | Object Oriented Programming | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling, File I/O and Templates |
| CS 203 | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics and Probability, Recurrence Relations |
| CS 204 | Computer Organization and Architecture | Core | 3 | Digital Logic Circuits, Data Representation, CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining |
| EC 201 | Digital Electronics | Core | 3 | Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Flip-Flops and Registers, Counters and Memory Devices |
| CS 205 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Stacks and Queues, Linked List Operations, Binary Search Tree Traversal, Graph Representation and Algorithms, Sorting and Searching Implementations |
| CS 206 | Object Oriented Programming Lab | Lab | 1 | Class and Object Creation, Implementing Inheritance and Polymorphism, Operator Overloading, Exception Handling in C++/Java, File I/O Operations |
| EC 202 | Digital Electronics Lab | Lab | 1 | Verification of Logic Gates, Design of Adders and Subtractors, Implementation of Multiplexers/Demultiplexers, Flip-Flop and Register Circuits, Counter Design |
| HS 201 | Universal Human Values | Humanities | 2 | Self-Exploration and Self-Awareness, Harmony in the Family and Society, Understanding Human Relationship, Coexistence with Nature, Ethical Human Conduct |
| HS 202 | Professional Ethics & IPR | Humanities | 2 | Ethical Theories and Dilemmas, Codes of Professional Ethics, Cyber Ethics and Security, Intellectual Property Rights Basics, Patents, Copyrights, and Trademarks |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 201 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Theory, Hypothesis Testing, Regression and Correlation |
| CS 251 | Design and Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms, NP-Completeness |
| CS 252 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks and Concurrency |
| CS 253 | Database Management Systems | Core | 3 | Relational Model, SQL Query Language, ER Diagrams and Schema Design, Normalization, Transaction Management, Concurrency Control |
| CS 254 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| CS 255 | Design and Analysis of Algorithms Lab | Lab | 1 | Implementation of Sorting Algorithms, Dynamic Programming Solutions, Greedy Algorithms, Graph Traversal Algorithms, Time and Space Complexity Analysis |
| CS 256 | Operating Systems Lab | Lab | 1 | Shell Scripting, System Calls Programming, Process Synchronization Problems, CPU Scheduling Simulation, Memory Allocation Strategies |
| CS 257 | Database Management Systems Lab | Lab | 1 | SQL Queries and Subqueries, Schema Definition and Manipulation, Data Definition Language (DDL), Data Manipulation Language (DML), Transaction Control Language (TCL) |
| HS 251 | Soft Skills and Personality Development | Humanities | 2 | Communication and Interpersonal Skills, Teamwork and Leadership, Time Management and Goal Setting, Stress Management and Emotional Intelligence, Public Speaking and Interview Skills |
| PE-I | Professional Elective – I | Elective | 1 | Advanced Data Structures, Computer Graphics, Digital Image Processing, Embedded Systems, Web Technology |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS 301 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS), Network Security Basics |
| CS 302 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Target Code Generation |
| CS 303 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms (BFS, DFS, A*), Knowledge Representation, Machine Learning Fundamentals, Natural Language Processing basics |
| CS 304 | Software Engineering | Core | 3 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Techniques, Project Management, Software Quality Assurance |
| CS 305 | Computer Networks Lab | Lab | 1 | Network Device Configuration, Socket Programming (TCP/UDP), Network Traffic Analysis, Routing Protocol Implementation, DNS and DHCP Configuration |
| CS 306 | Compiler Design Lab | Lab | 1 | Lexical Analyzer using Lex/Flex, Parser using Yacc/Bison, Symbol Table Management, Intermediate Code Generation, Code Optimization Techniques |
| CS 307 | Artificial Intelligence Lab | Lab | 1 | Implementing AI Search Algorithms, Logic Programming with Prolog, Machine Learning Libraries (Scikit-learn), Knowledge Representation Techniques, Mini AI Projects |
| PE-II | Professional Elective – II | Elective | 3 | Cloud Computing, Machine Learning, Deep Learning, Internet of Things, Blockchain Technology |
| OE-I | Open Elective – I | Elective | 3 | Marketing Management, Financial Management, Operations Research, Cyber Security Fundamentals, Artificial Intelligence for Everyone |
| HS 301 | Economics for Engineers | Humanities | 2 | Demand and Supply Analysis, Market Structures, Macroeconomics Fundamentals, Project Evaluation Techniques, Cost Analysis and Break-Even Analysis, Financial Statement Analysis |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS 351 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation Metrics, Ensemble Methods, Neural Networks Basics, Reinforcement Learning Introduction |
| CS 352 | Cryptography & Network Security | Core | 3 | Symmetric Key Cryptography (DES, AES), Asymmetric Key Cryptography (RSA), Hashing and Digital Signatures, Network Security Threats, Firewalls and Intrusion Detection Systems, Web Security |
| CS 353 | Data Warehousing & Data Mining | Core | 3 | Data Warehousing Concepts, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Techniques |
| CS 354 | Distributed Systems | Core | 3 | Distributed System Architectures, Interprocess Communication, Synchronization and Consistency, Fault Tolerance Mechanisms, Distributed File Systems, Cloud Computing Principles |
| CS 355 | Machine Learning Lab | Lab | 1 | Implementing Regression Models, Implementing Classification Models, Clustering Algorithms Practice, Using Scikit-learn and Pandas, Introduction to TensorFlow/PyTorch |
| CS 356 | Cryptography & Network Security Lab | Lab | 1 | Implementing Symmetric Key Algorithms, Implementing Asymmetric Key Algorithms, Digital Signature Generation, Network Scanning Tools (Nmap), Firewall Rule Configuration |
| CS 357 | Data Warehousing & Data Mining Lab | Lab | 1 | OLAP Cube Operations, Data Preprocessing and Cleaning, Implementing Association Rule Mining, Classification Algorithm Projects, Clustering Data Sets |
| PE-III | Professional Elective – III | Elective | 3 | Big Data Analytics, Quantum Computing, Robotics, Speech and Language Processing, Game Theory |
| OE-II | Open Elective – II | Elective | 3 | Entrepreneurship Development, Human Resource Management, Green Technology, Smart Cities Technologies, Introduction to Cyber Security |
| CS 358 | Industrial Training/Minor Project | Project | 2 | Project Planning and Scoping, Software Design and Implementation, Testing and Debugging, Documentation and Reporting, Problem Solving in Industry Context |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS 401 | Big Data Analytics | Core | 3 | Hadoop Ecosystem (HDFS, MapReduce), Apache Spark Framework, NoSQL Databases, Stream Data Processing, Data Visualization, Machine Learning with Big Data |
| CS 402 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization Technologies, Cloud Infrastructure Management, Public and Private Clouds, Cloud Security, Serverless Computing |
| PE-IV | Professional Elective – IV | Elective | 3 | Parallel and Distributed Computing, Internet of Everything, Software Defined Networks, Bio-informatics, Cognitive Computing |
| PE-V | Professional Elective – V | Elective | 3 | Cyber Physical Systems, Deep Reinforcement Learning, Computer Vision, Digital Forensics, Ethical Hacking |
| CS 403 | Major Project Part-I | Project | 3 | Problem Statement Definition, Literature Survey, Project Proposal Development, System Design and Architecture, Initial Implementation and Modules, Progress Reporting |
| CS 404 | Seminar/Industrial Visit | Project | 1 | Technical Presentation Skills, Report Preparation on Technical Topics, Industry Exposure and Case Studies, Understanding Latest Technology Trends, Professional Networking |
| CS 405 | Big Data Analytics Lab | Lab | 1 | Hadoop HDFS Commands and Operations, MapReduce Programming Exercises, Apache Spark Application Development, Hive and Pig Scripting, NoSQL Database Interaction |
| CS 406 | Cloud Computing Lab | Lab | 1 | Virtual Machine Creation and Management, Object Storage Configuration, IaaS/PaaS/SaaS Deployment, AWS/Azure/GCP Services Exploration, Cloud Security and Monitoring |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PE-VI | Professional Elective – VI | Elective | 3 | Agile Software Development, IoT Security, Natural Language Processing, Blockchain Technology Applications, Bioinformatics Fundamentals |
| OE-III | Open Elective – III | Elective | 3 | Project Management Techniques, Supply Chain Management, Intellectual Property Law, Financial Markets and Services, Disaster Management |
| CS 451 | Major Project Part-II | Project | 6 | Advanced Implementation and Integration, Comprehensive Testing and Debugging, Performance Optimization, Technical Report Writing, Project Demonstration and Defense, Innovation and Research Contribution |
| CS 452 | Dissertation/Internship | Project | 6 | In-depth Research Methodology, Literature Review and Problem Formulation, Data Analysis and Interpretation, Thesis Writing and Presentation, Real-world Industry Experience, Professional Skill Development |
