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B-TECH-M-TECH-INTEGRATED-DUAL-DEGREE in Computer Science And Engineering at Indian Institute of Technology (BHU) Varanasi
Varanasi, Uttar Pradesh
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About the Specialization
What is Computer Science and Engineering at Indian Institute of Technology (BHU) Varanasi Varanasi?
This Computer Science and Engineering integrated dual degree program at Indian Institute of Technology Banaras Hindu University Varanasi focuses on building a strong foundation in theoretical and applied aspects of computing. With India''''s booming digital economy, the program is designed to create skilled professionals adept at innovation, addressing the critical demand for advanced technological solutions in diverse sectors. It uniquely integrates B.Tech and M.Tech studies for comprehensive expertise and research orientation.
Who Should Apply?
This program is ideal for high-achieving 10+2 graduates with a strong aptitude for mathematics and problem-solving, aiming for deep specialization in computer science. It also caters to those aspiring for research or leadership roles in technology companies, offering a seamless transition from undergraduate to postgraduate studies. Enthusiastic coders, innovators, and future researchers looking to shape India''''s digital future will find this program highly rewarding and challenging.
Why Choose This Course?
Graduates of this program can expect to secure top-tier positions in product development, AI/ML engineering, cybersecurity, or data science across leading Indian and global MNCs. Entry-level salaries typically range from INR 10-25 lakhs annually, growing significantly with experience for this sought-after integrated degree. The comprehensive skillset provides advanced capabilities vital for contributing to India''''s technological advancements and potentially leading R&D teams and startups.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice programming concepts learned in CS101 and CS102. Focus on developing strong problem-solving logic, writing efficient code, and understanding algorithms. Actively participate in online coding challenges to build competitive programming skills early on.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, online C/C++ tutorials, previous years'''' problem sets
Career Connection
Strong programming fundamentals are non-negotiable for technical interviews and developing robust software solutions in any IT role.
Build a Strong Mathematical Base- (Semester 1-3)
Thoroughly understand foundational subjects like Mathematics-I, Mathematics-II, and Discrete Mathematics. These subjects form the bedrock for advanced algorithms, machine learning, cryptography, and theoretical computer science. Seek peer tutoring or faculty help for difficult concepts proactively.
Tools & Resources
NPTEL videos, Khan Academy, departmental tutorials, collaborative study groups
Career Connection
An excellent mathematical foundation is essential for roles in AI/ML, data science, research-oriented positions, and higher studies.
Engage in Interdisciplinary Exploration- (Semester 1-2)
Utilize the common first-year courses to explore interests beyond core CS. Understand the basics of other engineering disciplines (Electrical, Mechanical, Bioengineering). This broad perspective helps in identifying interdisciplinary project opportunities and understanding complex systems later in the program.
Tools & Resources
Elective course descriptions, guest lectures, college clubs (e.g., robotics, electronics), departmental open houses
Career Connection
Fosters innovative thinking, cross-functional collaboration, and systems-level understanding, valuable in diverse tech roles and product development.
Intermediate Stage
Deep Dive into Core CS Concepts- (Semester 3-5)
Focus intensely on Data Structures, Algorithms, Operating Systems, DBMS, and Computer Networks. Implement concepts from scratch, understand complexities, and solve competitive programming problems at a higher level. Build a strong theoretical and practical grasp of these core areas.
Tools & Resources
LeetCode, InterviewBit, standard textbooks (e.g., CLRS for Algorithms), personal projects on GitHub
Career Connection
These subjects are foundational for all software development and engineering roles and are heavily tested in almost all technical interviews.
Gain Practical Experience through Projects- (Semester 4-6)
Actively seek out and participate in departmental projects, mini-projects, or open-source contributions. Apply theoretical knowledge to build small to medium-scale applications or tools. Collaborate with peers on group projects to enhance teamwork and version control skills.
Tools & Resources
GitHub, departmental project mentorship, online project ideas (e.g., from Kaggle for ML), college technical festivals
Career Connection
Builds a strong project portfolio, crucial for internships, demonstrating practical skills, and showcasing problem-solving abilities.
Network and Explore Specializations- (Semester 3-6)
Attend departmental seminars, workshops, and guest lectures by industry experts. Connect with seniors, faculty, and alumni to understand various career paths within CSE. Start exploring potential areas for M.Tech specialization, such as AI, Cybersecurity, or Software Engineering.
Tools & Resources
LinkedIn, department events calendar, faculty office hours, alumni network portals, technical clubs
Career Connection
Helps in making informed career decisions, identifies mentorship opportunities, and builds a professional network valuable for internships and placements.
Advanced Stage
Master Technical Electives and Research- (Semester 6-9)
Choose technical electives wisely based on identified career interests (e.g., AI/ML, Cybersecurity, Cloud Computing, IoT). Engage deeply with these subjects, pursuing advanced projects or even research papers under faculty guidance, especially as part of the M.Tech curriculum.
Tools & Resources
Departmental research labs, arXiv, IEEE Xplore, faculty research groups, advanced online courses (e.g., Coursera, Udacity)
Career Connection
Develops specialized expertise for niche roles, prepares for research positions, or provides a strong foundation for pursuing a Ph.D.
Secure and Excel in Internships- (Semester 7-9 (during breaks or designated training period))
Actively pursue multiple internships (summer, winter, or industrial training) at reputable companies to gain extensive real-world experience. Focus on learning industry best practices, contributing meaningfully to projects, and networking with professionals and potential future employers.
Tools & Resources
College placement cell, LinkedIn Jobs, company career portals, mock interviews, resume building workshops
Career Connection
Often leads to pre-placement offers, provides invaluable industry exposure, strengthens the resume, and expands the professional network significantly.
Comprehensive Placement Preparation & M.Tech Dissertation- (Semester 8-10)
Begin focused preparation for placements (technical interviews, aptitude tests, HR rounds) in parallel with dedicating significant effort to the M.Tech dissertation (Part I & II). This involves deep research, robust implementation, rigorous analysis, and meticulous documentation, culminating in thesis defense.
Tools & Resources
Placement cell resources, mock interview platforms, company-specific preparation guides, research thesis guidelines, faculty supervisors and research mentors
Career Connection
Maximizes chances of securing desired jobs/research positions and culminates in a high-quality master''''s thesis, demonstrating advanced problem-solving and research capabilities.
Program Structure and Curriculum
Eligibility:
- Refer to JEE Advanced and JoSAA counselling guidelines for admission eligibility specific to IIT BHU.
Duration: 10 semesters / 5 years
Credits: 221 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CE101 | Engineering Mechanics | Core | 4 | Statics of Particles, Rigid Bodies, Equilibrium & Friction, Centroid & Moment of Inertia, Virtual Work |
| CY101 | Engineering Chemistry | Core | 4 | Atomic & Molecular Structure, Chemical Bonding, Electrochemistry & Corrosion, Spectroscopy, Stereochemistry |
| HS101 | Professional Communication | Core | 2 | Communication Process, Verbal & Non-Verbal Communication, Listening Skills, Public Speaking & Presentation, Interview Skills & Resume Writing |
| MA101 | Mathematics-I | Core | 4 | Calculus of One Variable, Mean Value Theorems, Partial Differentiation, Multiple Integrals, Vector Calculus |
| PH101 | Engineering Physics | Core | 4 | Wave Optics, Lasers & Fiber Optics, Quantum Mechanics, Solid State Physics, Special Theory of Relativity |
| ES101 | Engineering Drawing | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Computer Aided Drafting |
| CY102 | Engineering Chemistry Lab | Lab | 2 | Volumetric Analysis, Instrumental Methods of Analysis, Water Quality Parameters, Synthesis of Organic Compounds, Viscosity & Surface Tension |
| PH102 | Engineering Physics Lab | Lab | 2 | Optics Experiments, Semiconductor Device Characteristics, Magnetic Field Measurement, Resonance Phenomena, Physical Constants Measurement |
| ES102 | Workshop Practice | Lab | 2 | Carpentry, Fitting, Welding, Foundry, Machining Operations |
| AU101 | Physical Education | Core | 1 | Physical Fitness, Yoga & Meditation, Team Sports, Individual Sports, Health & Wellness |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BT101 | Introduction to Bioengineering | Core | 3 | Cell Biology, Biomolecules, Genetics & Biotechnology, Tissue Engineering, Biomechanics |
| CS101 | Introduction to Computing | Core | 3 | Programming Fundamentals, Variables & Data Types, Control Structures, Functions & Arrays, Pointers & Structures |
| EE101 | Basic Electrical Engineering | Core | 4 | DC Circuits & Network Theorems, AC Fundamentals, Transformers, DC & AC Machines, Single-Phase & Three-Phase Systems |
| HS102 | Psychology/Sociology | Elective | 2 | Social Institutions, Culture & Society, Social Stratification, Social Change, Research Methods in Sociology |
| MA102 | Mathematics-II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Fourier Series, Matrices & Linear Algebra, Vector Spaces |
| ME101 | Elements of Mechanical Engineering | Core | 4 | Thermodynamics, IC Engines, Refrigeration & Air Conditioning, Power Plants, Basic Mechanisms & Dynamics |
| CS102 | Computer Lab | Lab | 2 | C/C++ Programming Practice, Debugging Techniques, Algorithm Implementation, Problem Solving through Coding, Introduction to IDEs |
| EE102 | Basic Electrical Engineering Lab | Lab | 2 | Ohm''''s Law & KVL/KCL Verification, AC Circuit Analysis, Transformer Characteristics, Motor & Generator Experiments, Circuit Simulation |
| ME102 | Mechanical Engineering Lab | Lab | 2 | Heat Transfer Experiments, Fluid Mechanics Measurements, IC Engine Performance, Refrigeration System Analysis, Basic Lathe Operations |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS201 | Data Structures | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees & Binary Search Trees, Graphs & Traversal Algorithms, Hashing & Collision Resolution |
| CS202 | Discrete Mathematics | Core | 4 | Set Theory & Logic, Relations & Functions, Combinatorics & Recurrence Relations, Graph Theory, Algebraic Structures |
| CS203 | Object Oriented Programming | Core | 3 | Classes & Objects, Inheritance & Polymorphism, Encapsulation & Abstraction, Constructors & Destructors, Exception Handling & File I/O |
| EC201 | Digital Electronics | Core | 4 | Boolean Algebra & Logic Gates, Combinational Circuits, Sequential Circuits, Registers & Counters, Analog to Digital Conversion |
| MA201 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables & Distributions, Joint Distributions, Hypothesis Testing, Regression & Correlation |
| CS204 | Data Structures Lab | Lab | 2 | Stack & Queue Implementation, Linked List Operations, Tree Traversal Algorithms, Graph Algorithms Implementation, Hashing Techniques |
| CS205 | Object Oriented Programming Lab | Lab | 2 | C++ Programming Practice, Class & Object Design, Inheritance & Polymorphism Implementation, Operator Overloading, File I/O in C++ |
| EC202 | Digital Electronics Lab | Lab | 2 | Logic Gate Realization, Combinational Circuit Design, Sequential Circuit Implementation, Flip-Flops & Registers, Memory Interfacing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS251 | Theory of Computation | Core | 4 | Finite Automata & Regular Languages, Context-Free Grammars & Pushdown Automata, Turing Machines, Decidability & Undecidability, Complexity Classes (P, NP) |
| CS252 | Operating Systems | Core | 4 | Process Management & Scheduling, Deadlocks, Memory Management & Virtual Memory, File Systems & I/O Systems, Concurrency & Synchronization |
| CS253 | Database Management Systems | Core | 4 | ER Model & Relational Model, SQL Query Language, Normalization, Transaction Management, Concurrency Control & Recovery |
| CS254 | Computer Organization and Architecture | Core | 4 | Instruction Set Architecture, CPU Design & Pipelining, Memory Hierarchy, I/O Organization, Parallel Processing |
| EC251 | Microprocessor and Microcontroller | Core | 4 | 8085/8086 Architecture, Instruction Set & Assembly Language, Memory & I/O Interfacing, Interrupts, Introduction to Microcontrollers |
| CS255 | Operating Systems Lab | Lab | 2 | Linux Commands & Shell Scripting, Process & Thread Programming, Inter-Process Communication, System Calls, Memory Allocation Techniques |
| CS256 | Database Management Systems Lab | Lab | 2 | SQL Query Practice, Database Design & ER Diagram, Normalization Implementation, Stored Procedures & Triggers, Database Connectivity (e.g., JDBC) |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms & NP-Completeness |
| CS302 | Computer Networks | Core | 4 | OSI & TCP/IP Models, Physical & Data Link Layers, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols |
| CS303 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Project Management & Quality Assurance |
| CS304 | Artificial Intelligence | Core | 3 | Problem Solving & Search Algorithms, Knowledge Representation & Reasoning, Logic Programming, Machine Learning Basics, Natural Language Processing Fundamentals |
| EC301 | Control Systems | Core | 4 | System Modeling & Transfer Functions, Block Diagram & Signal Flow Graphs, Stability Analysis (Routh-Hurwitz, Nyquist), Root Locus Techniques, Bode Plots & PID Controllers |
| HSXXX | Humanities Elective-I | Elective | 2 | Varies by chosen elective from Humanities Department |
| CS305 | Algorithms Lab | Lab | 2 | Sorting & Searching Algorithms, Graph Traversal & Shortest Path, Dynamic Programming Problems, Greedy Algorithm Implementations, Algorithm Efficiency Analysis |
| CS306 | Computer Networks Lab | Lab | 2 | Socket Programming (TCP/UDP), Packet Analysis (Wireshark), Client-Server Application Development, Routing Protocol Configuration, Network Security Tools |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS351 | Compiler Design | Core | 4 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization & Code Generation |
| CS352 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation & Validation, Neural Networks Fundamentals, Ensemble Methods |
| CS353 | Cyber Security | Core | 3 | Cryptography & Network Security, Web Security, Malware & Vulnerabilities, Firewalls & IDS/IPS, Digital Forensics Basics |
| CS354 | Technical Elective I | Elective | 3 | Varies by chosen technical elective (e.g., Cloud Computing, Image Processing, IoT) |
| HSXXX | Humanities Elective-II | Elective | 2 | Varies by chosen elective from Humanities Department |
| CS355 | Compiler Design Lab | Lab | 2 | Lexical Analyzer (Lex/Flex), Parser Implementation (Yacc/Bison), Symbol Table Management, Intermediate Code Generation, Code Optimization Techniques |
| CS356 | Machine Learning Lab | Lab | 2 | Linear Regression Implementation, Classification Algorithms (SVM, Decision Trees), Clustering (K-Means), Introduction to Scikit-learn, Basic Neural Networks with Python |
| CS357 | Cyber Security Lab | Lab | 2 | Network Scanning Tools (Nmap), Vulnerability Assessment (OpenVAS), Cryptography Tools, Firewall Configuration, Web Application Security Testing |
| CS358 | Department Project-I | Project | 2 | Project Planning & Design, Literature Survey, Implementation & Testing, Documentation & Presentation, Problem Solving Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Parallel and Distributed Systems | Core | 3 | Parallel Architectures, Distributed Memory Systems, Message Passing Interface (MPI), Cloud Computing Basics, Distributed Algorithms & Consensus |
| CS402 | Technical Elective II | Elective | 3 | Varies by chosen technical elective |
| CS403 | Technical Elective III | Elective | 3 | Varies by chosen technical elective |
| CS404 | Open Elective-I | Elective | 3 | Varies by chosen open elective from any department |
| CS405 | Department Project-II | Project | 4 | Advanced Project Development, Research & Analysis, System Implementation & Evaluation, Comprehensive Report Writing, Oral Presentation & Demonstration |
| CS406 | Industrial Training | Training | 2 | Practical Industry Exposure, Application of Theoretical Knowledge, Industry Best Practices, Report Writing on Training Experience, Professional Networking |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS451 | Technical Elective IV | Elective | 3 | Varies by chosen technical elective |
| CS452 | Technical Elective V | Elective | 3 | Varies by chosen technical elective |
| CS453 | Open Elective-II | Elective | 3 | Varies by chosen open elective from any department |
| CS454 | Design Project | Project | 6 | Comprehensive System Design, Prototyping & Implementation, Testing & Validation, Detailed Project Report, Oral Examination & Presentation |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Advanced Data Structures and Algorithms | Core M.Tech | 3 | Amortized Analysis, Advanced Graph Algorithms, Network Flow, Randomized Algorithms, Computational Geometry |
| CS502 | Advanced Computer Architecture | Core M.Tech | 3 | Advanced Pipelining, Instruction Level Parallelism, Cache Coherence Protocols, Multiprocessor Architectures, Vector & Array Processors |
| CS5XX | M.Tech Elective-I (Example: Big Data Analytics) | Elective M.Tech | 3 | Big Data Ecosystem, Hadoop & MapReduce, Spark & Stream Processing, NoSQL Databases, Data Warehousing & Mining |
| CS5YY | M.Tech Elective-II (Example: Deep Learning) | Elective M.Tech | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Reinforcement Learning |
| CS500 | Dissertation Part-I | Project M.Tech | 6 | Literature Survey & Problem Formulation, Research Methodology, Preliminary Design & Experimentation, Progress Report & Presentation, Ethical Considerations in Research |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS5ZZ | M.Tech Elective-III (Example: Advanced Database Systems) | Elective M.Tech | 3 | Distributed Databases, Object-Oriented Databases, Data Warehousing & OLAP, Big Data Storage, Database Security & Privacy |
| CS5UU | M.Tech Elective-IV (Example: Internet of Things) | Elective M.Tech | 3 | IoT Architecture & Protocols, Sensor Networks, IoT Data Analytics, Security in IoT, Smart Applications & Case Studies |
| CS550 | Dissertation Part-II | Project M.Tech | 12 | In-depth Research & Development, Extensive Experimentation & Analysis, Thesis Writing & Documentation, Results Interpretation & Discussion, Final Dissertation Defense |
