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B-TECH in Computer Science Engineering at Panjab University
Chandigarh, Chandigarh
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About the Specialization
What is Computer Science Engineering at Panjab University Chandigarh?
This Computer Science Engineering program at Panjab University, Chandigarh, focuses on equipping students with a robust foundation in computing principles and their applications. It is meticulously designed to meet the dynamic needs of the Indian IT industry, emphasizing cutting-edge technologies like AI, Machine Learning, and Big Data. The program stands out for its blend of theoretical knowledge and practical skills, preparing graduates for key roles in technology innovation and development.
Who Should Apply?
This program is ideal for ambitious 10+2 graduates with a strong aptitude for mathematics and problem-solving, aspiring to build careers in the rapidly evolving tech sector. It also caters to individuals keen on research and development, seeking advanced computing expertise. Students from science backgrounds with a keen interest in software, data, and intelligent systems will find this curriculum highly rewarding.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including software development, data science, cybersecurity, and AI engineering. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more (INR 15-30+ LPA). The program fosters growth trajectories into lead engineering, architect, or managerial roles within top Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant time in semesters 1-2 to strengthen foundational programming skills using C/C++ and Object-Oriented Programming concepts. Actively solve problems on coding platforms to build logical thinking and algorithm implementation capabilities.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, Online C++ courses
Career Connection
Strong fundamentals are crucial for cracking coding rounds in placements and building efficient software solutions in any entry-level tech role.
Engage in Academic Peer Learning- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and prepare for exams. Collaboratively work on lab assignments to deepen understanding and develop teamwork skills, which are vital in industry.
Tools & Resources
Google Meet/Teams for virtual study, Whiteboards, UIET library resources
Career Connection
Enhances problem-solving through diverse perspectives and develops collaboration skills valued in corporate environments.
Develop Strong Communication Skills- (Semester 1-2)
Actively participate in communication skills labs and extra-curricular activities like debates or technical presentations. Focus on improving English proficiency, public speaking, and technical writing.
Tools & Resources
Toastmasters clubs (if available), English learning apps, TED Talks
Career Connection
Effective communication is a key differentiator in interviews and essential for clear client interaction and team collaboration in the workplace.
Intermediate Stage
Build a Diverse Project Portfolio- (Semester 3-5)
Start working on mini-projects from Semester 3 onwards, applying concepts from Data Structures, DBMS, and Operating Systems. Focus on creating functional prototypes that solve real-world problems or explore emerging technologies.
Tools & Resources
GitHub for version control, VS Code, MySQL/PostgreSQL, Python/Java
Career Connection
A robust portfolio showcases practical skills to recruiters, significantly boosting internship and placement chances, especially for specialized roles.
Seek Early Industry Internships- (Semester 3-5)
Actively apply for summer internships after the 3rd and 4th semesters, even if unpaid or in startups. Gain exposure to industrial workflows, professional tools, and corporate culture.
Tools & Resources
Internshala, LinkedIn Jobs, Company career pages, UIET placement cell
Career Connection
Internships provide invaluable practical experience, networking opportunities, and often lead to pre-placement offers, accelerating career entry.
Specialize and Certify in Key Domains- (Semester 3-5)
Identify areas of interest like AI/ML, Cybersecurity, or Web Development, and pursue online certifications. Complement academic learning with practical courses from platforms like Coursera or NPTEL.
Tools & Resources
Coursera, NPTEL, Udemy, edX, relevant vendor certifications (e.g., AWS, Azure)
Career Connection
Specialized skills and certifications make candidates highly marketable, opening doors to niche roles with better salary prospects in Indian tech firms.
Advanced Stage
Engage in Research or Major Project Development- (Semester 6-8)
Collaborate with faculty on research papers or develop substantial final-year projects that demonstrate innovation and comprehensive technical skills. Focus on problem-solving with advanced techniques like Deep Learning or Distributed Systems.
Tools & Resources
Research labs, academic conferences, Python with TensorFlow/PyTorch, cloud platforms
Career Connection
Distinguishes graduates for R&D roles, higher studies, or entrepreneurial ventures, showcasing advanced critical thinking and application abilities.
Intensive Placement Preparation- (Semester 6-8)
Dedicatedly prepare for technical interviews, aptitude tests, and group discussions. Practice mock interviews with faculty or alumni, and refine soft skills essential for corporate roles. Utilize UIET''''s placement cell resources thoroughly.
Tools & Resources
Placement cell workshops, online aptitude tests, interview prep books, Glassdoor
Career Connection
Crucial for securing desirable placements in top-tier companies, directly influencing initial career trajectory and salary packages.
Develop Professional Network and Mentorship- (Semester 6-8)
Attend industry seminars, tech talks, and alumni events to build a professional network. Seek mentorship from seniors or industry professionals to gain insights into career growth and industry trends.
Tools & Resources
LinkedIn, industry conferences, UIET alumni network, professional associations
Career Connection
Networking opens doors to hidden job opportunities, valuable career advice, and potential future collaborations, facilitating long-term career advancement in India.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics, Mathematics, and one of Chemistry/Biotechnology/Computer Science/Biology with at least 60% marks (55% for SC/ST/PwD) from a recognized board, and a valid JEE Main rank. Admissions typically through JAC Chandigarh.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40% (for theory courses), 50% (for practical courses), External: 60% (for theory courses), 50% (for practical courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ECS 101 | Engineering Physics | Core Theory | 4 | Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics, Electromagnetic Theory, Semiconductor Physics |
| EAS 101 | Mathematics - I | Core Theory | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Matrices and Determinants, Sequences and Series |
| EHS 101 | Environmental Science | Core Theory | 3 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Sustainable Development, Environmental Legislation |
| EET 101 | Basic Electrical Engineering | Core Theory | 4 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Basic Power Systems |
| EHM 101 | Human Values & Professional Ethics | Core Theory | 3 | Ethics in Engineering, Human Values, Moral Development Theories, Professional Responsibility, Societal Impact of Technology |
| EPR 101 | Introduction to Manufacturing Process | Core Theory | 3 | Casting Processes, Forming Processes, Machining Operations, Welding Techniques, Additive Manufacturing |
| ECS 151 | Engineering Physics Lab | Core Lab | 1 | Optical Experiments, Electronic Measurements, Semiconductor Device Characteristics, Magnetic Properties, Wave Phenomena |
| EET 151 | Basic Electrical Engineering Lab | Core Lab | 1 | Circuit Laws Verification, AC Circuit Analysis, Transformer Characteristics, DC Motor Control, Power Measurement |
| EPR 151 | Manufacturing Process Lab | Core Lab | 1 | Fitting Shop Practice, Carpentry Shop Practice, Welding Shop Practice, Foundry Shop Practice, Machine Shop Operations |
| ECS 152 | Engineering Graphics Lab | Core Lab | 2 | Orthographic Projections, Isometric Projections, Sectional Views, AutoCAD Fundamentals, Dimensioning and Tolerancing |
| EAS 151 | Communication Skills Lab | Core Lab | 1 | Public Speaking, Group Discussions, Technical Report Writing, Presentation Skills, Interview Techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EAS 201 | Mathematics - II | Core Theory | 4 | Linear Algebra, Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations |
| EAS 202 | Engineering Chemistry | Core Theory | 4 | Water Technology, Polymers and Composites, Corrosion and its Control, Fuels and Combustion, Electrochemistry |
| EAS 203 | Object Oriented Programming | Core Theory | 3 | Classes and Objects, Inheritance, Polymorphism, Encapsulation, Exception Handling |
| EAS 204 | Engineering Mechanics | Core Theory | 3 | Force Systems, Equilibrium of Rigid Bodies, Kinematics of Particles, Kinetics of Particles, Friction |
| EAS 205 | Basic Electronics | Core Theory | 4 | Semiconductor Devices, Diode Circuits, Transistor Amplifiers, Operational Amplifiers, Digital Logic Gates |
| EPR 201 | Workshop Practice | Core Theory | 3 | Sheet Metal Operations, Machine Tools, Measurements and Inspection, Safety Practices, CAD/CAM Introduction |
| EAS 251 | Engineering Chemistry Lab | Core Lab | 1 | Volumetric Analysis, Instrumental Analysis, Water Quality Testing, Polymer Synthesis, Corrosion Rate Determination |
| EAS 252 | Object Oriented Programming Lab | Core Lab | 1 | C++ Programming Basics, Class and Object Implementation, Inheritance Examples, Polymorphism Usage, File Handling |
| EAS 253 | Engineering Mechanics Lab | Core Lab | 1 | Verification of Laws of Mechanics, Friction Experiments, Moment of Inertia, Spring Testing, Beam Deflection |
| EAS 254 | Basic Electronics Lab | Core Lab | 1 | Diode Characteristics, Transistor biasing, Op-Amp Applications, Rectifier Circuits, Logic Gate Implementation |
| EAS 255 | Computer Aided Design | Core Lab | 1 | 2D Drafting using CAD software, 3D Modeling Fundamentals, Assembly Design, Rendering Techniques, Drawing Standards |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EAS 301 | Mathematics - III | Core Theory | 4 | Probability and Statistics, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| ECS 301 | Data Structures | Core Theory | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Hashing Techniques, Sorting and Searching Algorithms |
| ECS 302 | Digital Electronics | Core Theory | 4 | Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Flip-Flops and Counters, Analog-to-Digital Converters |
| ECS 303 | Computer Architecture and Organization | Core Theory | 4 | CPU Organization, Memory Hierarchy, Input/Output Organization, Instruction Set Architecture, Pipelining and Parallel Processing |
| EAS 302 | Engineering Economics | Core Theory | 3 | Demand and Supply Analysis, Cost Analysis, Market Structures, Project Evaluation, Inflation and Deflation |
| ECS 351 | Data Structures Lab | Core Lab | 1 | Array and Linked List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms, Sorting and Searching Implementations |
| ECS 352 | Digital Electronics Lab | Core Lab | 1 | Logic Gate Verification, Combinational Circuit Design, Sequential Circuit Implementation, Adder/Subtractor Circuits, Flip-Flop Applications |
| ECS 353 | Computer Architecture and Organization Lab | Core Lab | 1 | Assembly Language Programming, CPU Simulator, Memory Addressing Modes, Cache Simulation, Input/Output Interface |
| EAS 351 | Industrial Training - I (After 2nd Sem) | Practical/Training | 1.5 | Industrial Exposure, Basic Technical Skills, Workplace Etiquette, Project Documentation, Problem Solving in Industry |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EAS 401 | Numerical Methods and Optimization | Core Theory | 4 | Root Finding Methods, Interpolation and Extrapolation, Numerical Integration, Linear Programming, Optimization Techniques |
| ECS 401 | Design & Analysis of Algorithms | Core Theory | 4 | Algorithm Design Paradigms, Time and Space Complexity, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| ECS 402 | Operating Systems | Core Theory | 4 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks and Concurrency |
| ECS 403 | Database Management Systems | Core Theory | 4 | Relational Model, SQL Query Language, Database Design (ER Model), Normalization, Transaction Management |
| EAS 402 | Organizational Behavior | Core Theory | 3 | Individual Behavior, Group Dynamics, Leadership Theories, Motivation and Job Satisfaction, Organizational Culture |
| ECS 451 | Design & Analysis of Algorithms Lab | Core Lab | 1 | Sorting Algorithm Implementations, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithm Solutions, Complexity Analysis of Programs |
| ECS 452 | Operating Systems Lab | Core Lab | 1 | Process Creation and Management, CPU Scheduling Algorithms, Memory Allocation Strategies, File System Operations, Synchronization and Deadlock Solutions |
| ECS 453 | Database Management Systems Lab | Core Lab | 1 | SQL Query Writing, Database Schema Creation, Data Manipulation Language, Transaction Control, Stored Procedures and Triggers |
| ECS 454 | Industrial Training – II (After 3rd Sem) | Practical/Training | 1.5 | Industry Specific Tools, Project Development Lifecycle, Team Collaboration, Technical Report Writing, Problem Solving in Real-world Scenarios |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ECS 501 | Software Engineering | Core Theory | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing, Project Management |
| ECS 502 | Theory of Computation | Core Theory | 4 | Finite Automata, Context-Free Grammars, Turing Machines, Decidability and Undecidability, Complexity Classes (P, NP) |
| ECS 503 | Computer Networks | Core Theory | 4 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| ECS 504 | Artificial Intelligence | Core Theory | 4 | Problem Solving by Search, Knowledge Representation, Machine Learning Fundamentals, Natural Language Processing, Expert Systems |
| OEC-I | Open Elective I | Open Elective | 3 | Subject depends on chosen elective from other engineering disciplines or MOOCs offered by the university. |
| PEC-I | Professional Elective I | Professional Elective | 3 | Choice from Data Warehousing and Mining, Advanced Computer Architecture, Image Processing, Information Security. |
| ECS 551 | Software Engineering Lab | Core Lab | 1 | UML Diagramming, Software Design Patterns, Version Control Systems, Software Testing Tools, Agile Development Practices |
| ECS 552 | Computer Networks Lab | Core Lab | 1 | Network Configuration, Socket Programming, Packet Tracing, Routing Protocol Implementation, Network Security Tools |
| ECS 553 | Artificial Intelligence Lab | Core Lab | 1 | Search Algorithms Implementation, Knowledge Representation Techniques, Basic Machine Learning Models, Prolog/LISP Programming, AI Project Development |
| ECS 554 | Industrial Training – III (After 4th Sem) | Practical/Training | 1.5 | Advanced Software Development, System Integration, Industrial Best Practices, Mentorship and Feedback, Career Exploration |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ECS 601 | Compiler Design | Core Theory | 4 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| ECS 602 | Machine Learning | Core Theory | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Feature Engineering |
| ECS 603 | Web Technologies | Core Theory | 4 | HTML, CSS, JavaScript, Frontend Frameworks, Backend Development, Database Integration, Web Security |
| OEC-II | Open Elective II | Open Elective | 3 | Subject depends on chosen elective from other engineering disciplines or MOOCs offered by the university. |
| PEC-II | Professional Elective II | Professional Elective | 3 | Choice from Internet of Things, Cloud Computing, Parallel & Distributed Algorithms, Mobile Computing. |
| ECS 651 | Compiler Design Lab | Core Lab | 1 | Lexer Implementation (Flex/Lex), Parser Implementation (Yacc/Bison), Symbol Table Management, Intermediate Code Generation, Code Optimization Techniques |
| ECS 652 | Machine Learning Lab | Core Lab | 1 | Python for ML, Scikit-learn, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Data Preprocessing and Visualization |
| ECS 653 | Web Technologies Lab | Core Lab | 1 | Frontend Web Development, Backend API Development, Database Integration for Web Apps, Deployment Strategies, Responsive Design |
| ECS 654 | Major Project I | Project | 3 | Project Planning and Management, Requirement Analysis, System Design, Prototyping, Technical Documentation |
| ECS 655 | Industrial Training – IV (After 5th Sem) | Practical/Training | 1.5 | Specialized Domain Knowledge, Advanced Tool Proficiency, Project Management Skills, Mentoring and Leadership, Problem Identification and Solution |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ECS 701 | Distributed Systems | Core Theory | 4 | Distributed System Architectures, Inter-process Communication, Distributed Consensus, Fault Tolerance, Distributed Databases |
| ECS 702 | Big Data Analytics | Core Theory | 4 | Big Data Technologies (Hadoop, Spark), Data Warehousing Concepts, Data Mining Techniques, NoSQL Databases, Big Data Visualization |
| PEC-III | Professional Elective III | Professional Elective | 3 | Choice from Natural Language Processing, Block Chain Technology, High Performance Computing, Software Project Management. |
| PEC-IV | Professional Elective IV | Professional Elective | 3 | Choice from Cyber Security, Computer Vision, Digital Forensics, Genetic Algorithms. |
| OEC-III | Open Elective III | Open Elective | 3 | Subject depends on chosen elective from other engineering disciplines or MOOCs offered by the university. |
| ECS 751 | Distributed Systems Lab | Core Lab | 1 | RPC and RMI Implementations, Distributed File Systems, Message Queues, Distributed Transaction Protocols, Cloud Platform Deployment |
| ECS 752 | Big Data Analytics Lab | Core Lab | 1 | Hadoop Ecosystem (HDFS, MapReduce), Spark Programming, Data Loading and Processing, Machine Learning with Big Data, NoSQL Database Operations |
| ECS 753 | Major Project II | Project | 4 | Advanced Project Implementation, Module Integration, Testing and Debugging, Performance Optimization, Technical Presentation |
| ECS 754 | Industrial Training – V (After 6th Sem) | Practical/Training | 1.5 | Internship Project Execution, Industry Standard Tools, Professional Networking, Career Mentorship, Final Presentation and Report |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ECS 801 | Deep Learning | Core Theory | 4 | Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch), Generative Models |
| PEC-V | Professional Elective V | Professional Elective | 3 | Choice from Quantum Computing, Augmented Reality/Virtual Reality, Robotics, Game Theory. |
| OEC-IV | Open Elective IV | Open Elective | 3 | Subject depends on chosen elective from other engineering disciplines or MOOCs offered by the university. |
| ECS 851 | Deep Learning Lab | Core Lab | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Deep Learning Model Training, Hyperparameter Tuning |
| ECS 852 | Major Project III | Project | 6 | Full-scale System Development, Innovation and Research, Project Deployment, Comprehensive Documentation, Entrepreneurial Aspects |
| ECS 853 | Industrial Training – VI (After 7th Sem) | Practical/Training | 1.5 | Advanced Industrial Problem Solving, Leadership in Projects, Career Transition Planning, Ethics in Industry, Innovation and R&D Exposure |
