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BTECH in Computer Science Engineering at University of Lucknow
Lucknow, Uttar Pradesh
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About the Specialization
What is Computer Science Engineering at University of Lucknow Lucknow?
This B.Tech Computer Science Engineering program at University of Lucknow focuses on foundational computational theories and their practical application. It prepares students for the dynamic Indian IT industry, emphasizing algorithm design, software development, and emerging technologies like AI/ML, cloud computing, and IoT. The curriculum is designed to meet the evolving demands of the digital economy.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics, logical reasoning, and problem-solving, aspiring to build a career in technology. It''''s also suitable for individuals seeking to contribute to India''''s burgeoning tech sector, including freshers aiming for roles in software development, data science, or network administration. A background in science (PCM) is a prerequisite.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Software Developers, Data Analysts, AI/ML Engineers, Cybersecurity Specialists, or Cloud Architects. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more (INR 15-30+ LPA). The program fosters skills aligned with industry certifications from platforms like NPTEL and Coursera.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Diligent practice of C and Python programming daily is essential. Students should focus on understanding data types, control flow, functions, and basic algorithms. Regularly solving problems on online coding platforms is crucial for building a strong foundation.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on Programming in C/Python
Career Connection
Strong foundational programming skills are critical for clearing technical interviews and excelling in initial software development roles in the Indian IT landscape.
Build a Solid Math & Physics Base- (Semester 1-2)
Pay close attention to Engineering Mathematics I & II and Engineering Physics. These subjects build analytical and problem-solving skills essential for advanced CSE topics. Forming study groups to collectively tackle complex problems can significantly aid understanding.
Tools & Resources
Khan Academy, NPTEL, university study materials, peer discussion groups
Career Connection
A robust understanding of mathematical concepts supports advanced algorithms, data science, and AI/ML, crucial for R&D and specialized tech roles within India.
Engage in Practical Application- (Semester 1-2)
Actively participate in all labs, including Engineering Graphics, Physics, Programming, and Electrical Engineering. Apply theoretical knowledge to hands-on experiments and projects to understand the practical implications of engineering principles.
Tools & Resources
Lab manuals, relevant software (e.g., AutoCAD basics, circuit simulators), online tutorials
Career Connection
Practical exposure from early stages develops problem-solving aptitude and hands-on skills, making students job-ready for various entry-level engineering roles in India.
Intermediate Stage
Deep Dive into Core CS Subjects- (Semester 3-5)
Focus intensively on Data Structures & Algorithms, Operating Systems, DBMS, and Computer Networks. Implement concepts in labs and apply them to mini-projects. Understand the internal working and trade-offs of different approaches thoroughly.
Tools & Resources
LeetCode, InterviewBit, official documentation for SQL/Linux, relevant textbooks
Career Connection
Mastery of these core subjects is non-negotiable for most software development, backend engineering, and system design roles in the Indian IT industry.
Participate in Hackathons & Coding Competitions- (Semester 3-5)
Actively join college-level, inter-collegiate, and national hackathons (e.g., Smart India Hackathon) and coding competitions. This provides real-world problem exposure, fosters teamwork, and helps in building a compelling project portfolio.
Tools & Resources
Devpost, GitHub, online judges like Codeforces, Google Kick Start
Career Connection
Showcases problem-solving abilities, innovation, and teamwork, which are highly valued by Indian tech recruiters, often leading to direct interview calls and internships.
Explore Specializations via Electives & Mini Projects- (Semester 5)
Utilize departmental electives (e.g., AI, Data Compression, Cloud Computing) and mini-projects to explore areas of interest. Build small applications or conduct research to gain deeper insights into chosen specialized fields.
Tools & Resources
Open-source libraries (e.g., scikit-learn), online courses (Coursera, edX), project documentation
Career Connection
Early specialization helps in defining career goals, building relevant skills, and creating a project portfolio that differentiates candidates for specific roles in the competitive Indian job market.
Advanced Stage
Secure and Excel in Internships- (Semester 6-8 (especially summer breaks))
Actively seek and complete multiple internships in relevant companies, both startups and established firms in India. Apply academic knowledge to real-world challenges, build industry connections, and gain invaluable practical experience.
Tools & Resources
LinkedIn, Internshala, company career pages, university placement cell
Career Connection
Internships are often the direct pathway to pre-placement offers (PPOs) in Indian companies and provide crucial experience for future job applications and accelerating career growth.
Focus on Major Project Development- (Semester 7-8)
Dedicate significant effort to the Major Project (Part I & II). Choose a topic aligned with career aspirations (e.g., AI/ML, Cybersecurity, IoT) and develop a robust, innovative solution. Present regularly and seek mentor feedback.
Tools & Resources
GitHub for version control, project management tools, relevant frameworks/libraries, faculty mentors
Career Connection
A well-executed major project serves as a powerful resume booster, demonstrating advanced skills and problem-solving capabilities to potential employers, especially for R&D or specialized tech roles.
Prepare for Placements & Higher Studies- (Semester 7-8)
Systematically prepare for campus placements, focusing on aptitude tests, technical interviews (DS/Algo, OS, Networks, DBMS), and soft skills. Simultaneously explore options for M.Tech/MS in India or abroad, preparing for exams like GATE/GRE.
Tools & Resources
Placement training modules, mock interviews, company-specific preparation guides, counseling from career services
Career Connection
This direct path leads to securing a good job in India''''s top tech companies or pursuing advanced academic qualifications, enabling long-term career progression and specialization.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 45% marks (40% in case of candidates belonging to reserved category) in the above subjects taken together.
Duration: 4 years / 8 semesters
Credits: 174 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KAS103 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Vector Calculus, Ordinary Differential Equations |
| KAS101T | Engineering Physics | Core | 3 | Relativistic Mechanics, Electromagnetic Field Theory, Quantum Mechanics, Laser, Fiber Optics |
| KCS101T | Programming for Problem Solving | Core | 3 | Introduction to C, Control Structures, Functions, Arrays and Pointers, Structures and File Handling |
| KNC101 | Universal Human Values | Mandatory | 1 | Understanding Self, Harmony with Self and Family, Harmony with Society, Harmony with Nature, Ethical Human Conduct |
| KAS105T | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Power Converters |
| KAS101P | Engineering Physics Lab | Lab | 1 | Experiments related to optics, mechanics, electricity |
| KCS101P | Programming for Problem Solving Lab | Lab | 1 | C programming exercises, debugging |
| KEC101P | Basic Electrical Engineering Lab | Lab | 1 | Verification of network theorems, AC/DC circuit analysis |
| KME101P | Engineering Graphics & Design Lab | Lab | 1 | Orthographic projections, Sectional views, Isometric views |
| KNC101P | Universal Human Values Lab | Lab | 1 | Group discussions, case studies on human values |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KAS203 | Engineering Mathematics-II | Core | 4 | Multivariable Calculus, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Analysis |
| KAS202T | Engineering Chemistry | Core | 3 | Water Treatment, Spectroscopic Techniques, Organic Reactions, Polymer Chemistry, Corrosion |
| KAS204T | Environmental Science & Basic Civil Engg. | Core | 3 | Environmental Pollution, Water Resources, Ecosystems, Building Materials, Engineering Mechanics |
| KAS206T | Python Programming for Engineering | Elective | 3 | Python Fundamentals, Data Structures in Python, Object-Oriented Programming with Python, File Handling, Numerical Computing with NumPy |
| KAS202P | Engineering Chemistry Lab | Lab | 1 | Experiments on water analysis, viscosity, surface tension |
| KCS221P | Python Programming Lab | Lab | 1 | Python programming exercises, data manipulation, scripting |
| KME202P | Workshop Practice | Lab | 1 | Fitting, Carpentry, Welding, Machining, Foundry |
| KNC202 | Professional Communication | Mandatory | 1 | Basic English Grammar, Communication Skills, Technical Writing, Presentation Skills, Interview Techniques |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS301 | Data Structures & Algorithms | Core | 3 | Arrays, Stacks, Queues, Linked Lists, Trees and Heaps, Graphs, Sorting & Searching |
| KCS302 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| KCS303 | Computer Organization & Architecture | Core | 3 | Computer System Basics, CPU Organization, Memory Hierarchy, I/O Organization, Pipelining and Parallel Processing |
| KCS304 | Operating System | Core | 3 | OS Concepts and Services, Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems |
| KNC301 | Cyber Security | Mandatory | 2 | Introduction to Cyber Security, Network Security, Web Security, Cryptography Basics, Cyber Laws |
| KAS301 | Engineering Mathematics-III | Core | 3 | Laplace Transforms, Fourier Transforms, Complex Analysis, Probability Distributions, Statistical Inference |
| KCS351 | Data Structures & Algorithms Lab | Lab | 1 | Implementation of arrays, stacks, queues, Linked lists operations, Tree traversals, Graph algorithms, Sorting and searching implementations |
| KCS352 | Operating System Lab | Lab | 1 | Shell programming, Process management commands, CPU scheduling simulations, Memory management techniques, File system operations |
| KCS353 | Computer Organization & Architecture Lab | Lab | 1 | Logic Gates and Boolean Algebra, Combinational Circuits, Sequential Circuits, Memory organization concepts, Assembly language programming basics |
| KNC351 | Cyber Security Lab | Lab | 1 | Network scanning tools, Vulnerability assessment, Firewall configurations, Basic cryptography tools, Secure coding practices |
| KNC352 | Computer Aided Design Lab | Lab | 1 | Introduction to CAD software, 2D drafting, 3D modeling basics, Assembly drawing, Design analysis fundamentals |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS401 | Theory of Automata and Formal Languages | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| KCS402 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| KCS403 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management |
| KCS404 | Object Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, STL |
| KNC401 | Constitution of India | Mandatory | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Legislature, Judiciary in India, Emergency Provisions |
| KAS402 | Industrial Psychology | Elective | 3 | Industrial Behavior, Motivation Theories, Leadership Styles, Stress Management, Organizational Culture |
| KCS451 | Design and Analysis of Algorithms Lab | Lab | 1 | Implementation of sorting algorithms, Greedy algorithm examples, Dynamic programming solutions, Graph traversal algorithms, Time and space complexity analysis |
| KCS452 | Software Engineering Lab | Lab | 1 | Requirements gathering tools, UML diagramming, Software design patterns, Testing frameworks, Project planning and tracking |
| KCS453 | Object Oriented Programming Lab | Lab | 1 | Class and object implementation, Inheritance and polymorphism exercises, Exception handling in C++/Java, File I/O operations, Generic programming with STL |
| KNC451 | Python Programming Lab | Lab | 1 | Advanced Python scripting, Data visualization libraries, Web scraping, GUI programming basics, Database connectivity with Python |
| KNC452 | Soft Skill & Interpersonal Communication Lab | Lab | 1 | Group discussions, Role-playing exercises, Public speaking practice, Resume building, Interview etiquette |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS501 | Database Management System | Core | 3 | DBMS Architecture, ER Model, Relational Model, SQL Query Language, Normalization, Transaction Management |
| KCS502 | Computer Networks | Core | 3 | Network Topologies, OSI/TCP-IP Models, Data Link Layer, Network Layer Protocols, Transport Layer, Application Layer Protocols |
| KCS503 | Web Technology | Core | 3 | HTML and CSS, JavaScript for Client-Side, Web Servers and HTTP, Server-Side Scripting (e.g., PHP/Node.js), Database Connectivity, Web Security Basics |
| KCS504 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| KCS051 | Data Compression | Departmental Elective-I | 3 | Information Theory, Lossless Compression Techniques, Lossy Compression Techniques, JPEG Image Compression, MPEG Video Compression, Data Security aspects |
| KCS551 | Database Management System Lab | Lab | 1 | SQL DDL and DML commands, Database creation and manipulation, Joins and subqueries, Stored procedures and triggers, Transaction control |
| KCS552 | Computer Networks Lab | Lab | 1 | Network configuration commands, Socket programming, Packet analysis with Wireshark, Routing protocols simulation, Network security tools |
| KCS553 | Web Technology Lab | Lab | 1 | HTML/CSS page design, JavaScript interactive elements, Server-side scripting with PHP/Node.js, Database integration for web apps, Deployment to a web server |
| KCS554 | Artificial Intelligence Lab | Lab | 1 | Search algorithm implementations, Constraint satisfaction problems, Prolog programming basics, Machine learning library usage (Scikit-learn), AI project development |
| KNC551 | Project Based Learning/Mini Project | Project | 2 | Problem identification, Requirement analysis, Design and implementation, Testing and debugging, Documentation and presentation |
| KNC552 | Industrial Training/Internship | Practical | 2 | Real-world project experience, Industry work environment, Professional skill development, Teamwork and collaboration, Report writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS601 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization, Runtime Environments |
| KCS602 | Cryptography and Network Security | Core | 3 | Classical Ciphers, Symmetric Key Cryptography, Asymmetric Key Cryptography, Hash Functions, Digital Signatures, Firewalls and IDS |
| KCS603 | Distributed System | Core | 3 | Distributed Architecture, Remote Procedure Call, Distributed File Systems, Concurrency Control, Replication and Consistency, Fault Tolerance |
| KCS061 | Cloud Computing | Departmental Elective-II | 3 | Cloud Architecture, Virtualization Technology, Cloud Services (IaaS, PaaS, SaaS), Cloud Security, Cloud Deployment Models, Cloud Migration Strategies |
| KOC051 | Entrepreneurship Development | Open Elective-I | 3 | Entrepreneurial Process, Business Plan Development, Market Analysis, Financial Management for Startups, Legal Aspects of Business, Innovation and Creativity |
| KCS651 | Compiler Design Lab | Lab | 1 | Lexical analyzer implementation, Parsing techniques (e.g., LL/LR), Syntax tree generation, Intermediate code generation, Simple code optimization |
| KCS652 | Cryptography and Network Security Lab | Lab | 1 | Encryption/Decryption using AES/RSA, Hash function implementation, Digital signature schemes, Network intrusion detection, Firewall rule configuration |
| KCS653 | Distributed System Lab | Lab | 1 | RPC/RMI implementation, Distributed file system experiments, Concurrency control in distributed systems, Fault tolerance mechanisms, Client-server programming |
| KCS654 | Minor Project | Project | 2 | Problem definition, Literature review, System design, Implementation, Testing and evaluation |
| KNC651 | General Proficiency | Mandatory | 1 | Aptitude development, Current affairs knowledge, Logical reasoning skills, Interview preparation, Personality development |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS701 | Data Science | Core | 3 | Data Collection and Preprocessing, Exploratory Data Analysis, Machine Learning Algorithms, Data Visualization, Big Data Technologies, Statistical Inference for Data Science |
| KCS702 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Neural Networks Basics, Model Evaluation and Validation, Feature Engineering |
| KCS071 | Soft Computing | Departmental Elective-III | 3 | Fuzzy Logic Systems, Artificial Neural Networks, Genetic Algorithms, Hybrid Soft Computing Systems, Rough Set Theory, Swarm Intelligence |
| KCS074 | Mobile Computing | Departmental Elective-IV | 3 | Mobile Architecture, Wireless Technologies (GSM, GPRS, 3G, 4G), Mobile Operating Systems, Mobile Application Development, Location-Based Services, Mobile Security |
| KOC071 | Energy Science & Engineering | Open Elective-II | 3 | Conventional Energy Sources, Renewable Energy Technologies, Energy Conservation, Environmental Impact of Energy, Smart Grids and Energy Storage, Energy Policy and Management |
| KCS751 | Data Science Lab | Lab | 1 | Data cleaning with Pandas, Data visualization with Matplotlib/Seaborn, Statistical analysis with NumPy/SciPy, Basic ML model training, Big data tool fundamentals (e.g., Hadoop HDFS) |
| KCS752 | Machine Learning Lab | Lab | 1 | Supervised learning algorithms (regression, classification), Unsupervised learning algorithms (clustering), Neural network implementation (TensorFlow/Keras), Model evaluation metrics, Cross-validation techniques |
| KCS753 | Departmental Elective-III Lab | Lab | 1 | Fuzzy logic system design, Neural network training, Genetic algorithm implementation, Hybrid system applications, Soft computing project development |
| KCS754 | Project Part-I | Project | 4 | Project proposal development, Detailed system design, Initial implementation/prototype, Regular progress reporting, Team collaboration and documentation |
| KCS755 | Industrial Training/Internship | Practical | 2 | Advanced industry project execution, Application of theoretical knowledge, Exposure to industry best practices, Networking with professionals, Comprehensive report submission |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| KCS801 | Internet of Things | Core | 3 | IoT Architecture, Sensors and Actuators, IoT Communication Protocols (MQTT, CoAP), IoT Platforms (AWS IoT, Azure IoT), Data Analytics for IoT, IoT Security and Privacy |
| KCS802 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Reinforcement Learning, Transfer Learning |
| KCS082 | Natural Language Processing | Departmental Elective-V | 3 | Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Syntactic Parsing, Sentiment Analysis, Machine Translation |
| KOC081 | Disaster Management | Open Elective-III | 3 | Types of Disasters, Disaster Management Cycle, Risk Assessment and Vulnerability Analysis, Mitigation Strategies, Disaster Preparedness and Response, Rehabilitation and Reconstruction |
| KCS851 | Internet of Things Lab | Lab | 1 | Sensor interfacing with microcontrollers, Data transmission using IoT protocols, Cloud platform integration (e.g., Node-RED), IoT device programming, Building simple IoT applications |
| KCS852 | Deep Learning Lab | Lab | 1 | CNN implementation for image classification, RNN implementation for sequence tasks, TensorFlow/Keras model building, Hyperparameter tuning, Deep learning project development |
| KCS853 | Major Project | Project | 10 | Advanced system design, Full-scale implementation, Rigorous testing and validation, Comprehensive documentation, Research paper/publication (optional) |
| KNC851 | General Proficiency | Mandatory | 1 | Advanced aptitude skills, Industry-specific knowledge, Leadership and teamwork skills, Career planning, Professional ethics |
