B.Tech. Computer Science and Engineering (Lateral Entry)

Fee Type	Amount
Programme Fees (per Semester)	₹75000
Examination Fees	₹3000
International Fee (USD)	$4300

Slab	>=60% - 74.99%	>=75% - 89.99%	>=90% & Above
Fee	₹70000	₹65000	₹60000

Course Curriculum

3RD SEMESTER SUBJECTS

Covers fundamentals of database design, the relational model, SQL, normalization, and transaction management.

Course Outcome:

CO1: Understand basic concepts of database systems and the relational data model.
CO2: Design ER diagrams and convert them into relational schemas.
CO3: Formulate queries using SQL and relational algebra.
CO4: Apply normalization techniques to design efficient, redundancy-free databases.
CO5: Understand transaction management, concurrency control, and recovery techniques.
CO6: Explore concepts of indexing and query optimization.

Hands-on lab for designing databases and implementing SQL queries using RDBMS tools.

Course Outcome:

CO1: Create and manipulate databases using DDL and DML commands.
CO2: Write and execute simple to complex SQL queries.
CO3: Implement joins, subqueries, and views.
CO4: Design and implement PL/SQL procedures, functions, and triggers.
CO5: Apply normalization and ER modeling to real-world case studies.

Introduces linear and non-linear data structures along with algorithm design and analysis techniques.

Course Outcome:

CO1: Understand asymptotic notations and analyze the time/space complexity of algorithms.
CO2: Implement linear data structures such as arrays, stacks, queues, and linked lists.
CO3: Implement non-linear data structures such as trees and graphs.
CO4: Apply searching and sorting algorithms to solve computational problems.
CO5: Understand hashing techniques and their applications.
CO6: Design efficient algorithms using divide-and-conquer, greedy, and dynamic programming approaches.

Lab-based implementation of data structures and algorithmic problem-solving using a programming language.

Course Outcome:

CO1: Implement stacks, queues, and linked lists programmatically.
CO2: Implement tree and graph traversal algorithms.
CO3: Implement and compare various sorting and searching techniques.
CO4: Apply data structures to solve real-world computational problems.
CO5: Analyze the time complexity of implemented algorithms.

Covers advanced mathematical techniques including transforms, numerical methods, and probability essential for engineering applications.

Course Outcome:

CO1: Apply Laplace and Fourier transforms to solve engineering problems.
CO2: Solve differential equations using series solutions and special functions.
CO3: Apply numerical methods for solving algebraic and differential equations.
CO4: Understand concepts of probability and statistical distributions.
CO5: Apply vector calculus concepts to engineering problems.

Introduces core AI concepts, search techniques, knowledge representation, and intelligent agent design.

Course Outcome:

CO1: Understand the fundamentals of AI and intelligent agents.
CO2: Apply uninformed and informed search techniques to solve problems.
CO3: Represent knowledge using logic-based and semantic approaches.
CO4: Understand the basics of machine learning and its role in AI systems.
CO5: Apply AI techniques to design simple intelligent systems.
CO6: Understand ethical and societal implications of AI.

Builds entrepreneurial thinking, business planning, and innovation skills through practical exposure.

Course Outcome:

CO1: Understand the entrepreneurial mindset and identify business opportunities.
CO2: Develop a basic business model and plan for a venture idea.
CO3: Understand fundamentals of innovation, risk-taking, and value creation.
CO4: Apply financial and marketing basics to a proposed venture.
CO5: Present and pitch a business idea effectively.

Covers web development fundamentals including HTML, CSS, JavaScript, and modern frontend frameworks.

Course Outcome:

CO1: Design responsive web pages using HTML5 and CSS3.
CO2: Apply JavaScript for dynamic client-side interactivity.
CO3: Understand DOM manipulation and event handling.
CO4: Build responsive layouts using frameworks such as Bootstrap.
CO5: Develop simple single-page applications using a modern frontend framework.
CO6: Understand principles of UI/UX design for web applications.

Hands-on lab for building responsive and interactive web pages using HTML, CSS, JavaScript, and frontend frameworks.

Course Outcome:

CO1: Design and implement static and responsive web pages.
CO2: Implement client-side scripting using JavaScript.
CO3: Build interactive web components using DOM manipulation.
CO4: Develop a mini-project using a frontend framework.
CO5: Apply version control basics for web project development.

Introduces foundational concepts of Artificial Intelligence including problem-solving, search, and reasoning techniques.

Course Outcome:

CO1: Understand the history, scope, and applications of AI.
CO2: Apply problem-solving techniques using search strategies.
CO3: Understand knowledge representation and reasoning approaches.
CO4: Understand the basics of machine learning algorithms.
CO5: Explore real-world applications of AI across domains.

Practical lab exposure to AI programming tools, libraries, and basic machine learning implementations.

Course Outcome:

CO1: Implement basic AI search algorithms programmatically.
CO2: Use Python libraries for data handling and preprocessing.
CO3: Implement simple machine learning models using standard datasets.
CO4: Evaluate model performance using basic metrics.
CO5: Apply AI tools to solve a mini real-world problem.

4TH SEMESTER SUBJECTS

Covers algorithm design paradigms, complexity analysis, and advanced algorithmic techniques for problem-solving.

Course Outcome:

CO1: Analyze the time and space complexity of algorithms using asymptotic notations.
CO2: Design algorithms using divide-and-conquer, greedy, and dynamic programming strategies.
CO3: Apply graph algorithms for shortest path, spanning tree, and network flow problems.
CO4: Understand backtracking and branch-and-bound techniques.
CO5: Analyze NP-completeness and classify problems based on computational complexity.
CO6: Design and evaluate efficient algorithms for real-world computational problems.

Lab-based implementation and performance analysis of algorithm design techniques.

Course Outcome:

CO1: Implement divide-and-conquer algorithms and analyze their performance.
CO2: Implement greedy and dynamic programming based solutions.
CO3: Implement graph algorithms for traversal, shortest path, and spanning trees.
CO4: Implement backtracking algorithms for constraint satisfaction problems.
CO5: Compare and evaluate algorithm efficiency using empirical analysis.

Introduces networking concepts, protocols, and architectures across the OSI and TCP/IP layers.

Course Outcome:

CO1: Understand network architectures, topologies, and the OSI/TCP-IP reference models.
CO2: Analyze data link layer protocols including error detection and correction techniques.
CO3: Understand network layer concepts including routing algorithms and IP addressing.
CO4: Analyze transport layer protocols and congestion control mechanisms.
CO5: Understand application layer protocols and their real-world implementations.
CO6: Explore network security fundamentals and emerging networking technologies.

Hands-on lab for network configuration, simulation, and protocol analysis.

Course Outcome:

CO1: Configure basic networking devices and IP addressing schemes.
CO2: Simulate network topologies using networking tools/simulators.
CO3: Analyze network traffic and protocols using packet capture tools.
CO4: Implement socket programming for client-server communication.
CO5: Troubleshoot common networking issues in a lab environment.

Covers mathematical foundations including set theory, logic, graph theory, and combinatorics for computer science.

Course Outcome:

CO1: Apply propositional and predicate logic to solve reasoning problems.
CO2: Understand set theory, relations, and functions.
CO3: Apply combinatorics and counting principles to solve problems.
CO4: Understand graph theory concepts including trees, graphs, and their applications.
CO5: Apply algebraic structures such as groups and lattices to computer science problems.

Introduces operating system concepts including process management, memory management, and file systems.

Course Outcome:

CO1: Understand the structure, functions, and types of operating systems.
CO2: Apply process scheduling algorithms and understand process synchronization.
CO3: Understand deadlock detection, prevention, and avoidance techniques.
CO4: Apply memory management techniques including paging and segmentation.
CO5: Understand file system organization and disk scheduling algorithms.
CO6: Explore concepts of virtualization and modern operating systems.

Hands-on lab for implementing OS concepts using shell scripting and system calls.

Course Outcome:

CO1: Implement basic Linux/Unix commands and shell scripts.
CO2: Implement process creation and synchronization using system calls.
CO3: Implement CPU scheduling algorithms programmatically.
CO4: Implement memory management techniques such as paging.
CO5: Implement solutions for classical process synchronization problems.

Covers computer system architecture, instruction sets, memory hierarchy, and processor design.

Course Outcome:

CO1: Understand basic computer organization and functional units.
CO2: Analyze instruction set architectures and addressing modes.
CO3: Understand arithmetic and logic unit design and computer arithmetic.
CO4: Understand memory hierarchy including cache and virtual memory.
CO5: Understand pipelining and instruction-level parallelism.
CO6: Explore I/O organization and multiprocessor architectures.

Covers full-stack web development using MongoDB, Express.js, React, and Node.js.

Course Outcome:

CO1: Design responsive front-end interfaces using React.
CO2: Build RESTful APIs using Node.js and Express.js.
CO3: Design and integrate MongoDB databases with web applications.
CO4: Implement authentication and state management in MERN applications.
CO5: Deploy full-stack web applications using modern deployment practices.

Hands-on lab for building full-stack applications using the MERN stack.

Course Outcome:

CO1: Build React components and manage application state.
CO2: Develop and test RESTful APIs using Express.js and Node.js.
CO3: Design and query MongoDB collections.
CO4: Integrate front-end and back-end components into a full-stack application.
CO5: Develop a mini-project using the complete MERN stack.

Introduces SEO principles, techniques, and tools for improving website visibility and ranking.

Course Outcome:

CO1: Understand fundamentals of search engines and ranking algorithms.
CO2: Apply on-page SEO techniques including keyword research and content optimization.
CO3: Apply off-page SEO techniques including link building and outreach.
CO4: Analyze website performance using SEO tools and analytics.
CO5: Understand technical SEO concepts including site structure and mobile optimization.

Practical lab for implementing SEO strategies on real or sample websites.

Course Outcome:

CO1: Perform keyword research and competitor analysis.
CO2: Implement on-page optimization techniques on a sample website.
CO3: Use SEO tools to audit and analyze website performance.
CO4: Implement basic link-building and content marketing strategies.
CO5: Prepare an SEO performance report using analytics tools.

Builds entrepreneurial thinking and practical business skills through case studies and projects.

Course Outcome:

CO1: Understand advanced concepts of entrepreneurship and business growth.
CO2: Analyze case studies of successful startups and ventures.
CO3: Develop strategies for scaling and sustaining a business.
CO4: Apply leadership and team-building concepts to entrepreneurial ventures.
CO5: Present a refined business plan incorporating market feedback.

5TH SEMESTER SUBJECTS

Covers software development life cycle, requirement analysis, design, and project management principles.

Course Outcome:

CO1: Understand software process models and the software development life cycle.
CO2: Apply requirement elicitation and analysis techniques.
CO3: Apply software design principles including modularity and design patterns.
CO4: Understand software testing techniques and quality assurance practices.
CO5: Apply project management and estimation techniques for software projects.
CO6: Understand software maintenance and configuration management.

Introduces formal languages, automata theory, and computability concepts.

Course Outcome:

CO1: Understand finite automata and regular languages.
CO2: Design context-free grammars and analyze pushdown automata.
CO3: Understand Turing machines and their role in computability.
CO4: Classify problems based on decidability and undecidability.
CO5: Understand complexity classes including P and NP.

Covers object-oriented programming concepts and application development using Java.

Course Outcome:

CO1: Understand the history, features, and fundamental elements of Java programming.
CO2: Apply object-oriented concepts including classes, objects, and inheritance.
CO3: Implement interfaces, packages, and exception handling in Java.
CO4: Understand multithreading and concurrent programming in Java.
CO5: Implement GUI applications using AWT/Swing.
CO6: Handle file I/O and stream operations in Java.

Hands-on lab for developing Java applications using object-oriented concepts.

Course Outcome:

CO1: Implement basic Java programs using core language constructs.
CO2: Implement classes, objects, and inheritance-based programs.
CO3: Implement exception handling and multithreading programs.
CO4: Develop GUI-based applications using Java.
CO5: Implement file handling operations in Java.

Covers information security principles, cryptography basics, and cyber law frameworks.

Course Outcome:

CO1: Understand fundamentals of information security and threat models.
CO2: Apply cryptographic techniques for data confidentiality and integrity.
CO3: Understand network and application security mechanisms.
CO4: Understand cyber laws, IT Act provisions, and compliance requirements.
CO5: Analyze case studies of cybercrimes and security breaches.

Introduces ethical hacking methodologies, tools, and penetration testing techniques.

Course Outcome:

CO1: Understand the fundamentals and phases of ethical hacking.
CO2: Perform reconnaissance and scanning using penetration testing tools.
CO3: Identify and exploit common vulnerabilities in systems and networks.
CO4: Understand web application security testing techniques.
CO5: Prepare penetration testing reports following ethical and legal guidelines.

Hands-on lab for practicing ethical hacking and penetration testing techniques.

Course Outcome:

CO1: Perform network scanning and vulnerability assessment using standard tools.
CO2: Exploit system and application vulnerabilities in a controlled lab environment.
CO3: Perform web application penetration testing.
CO4: Use password cracking and social engineering awareness techniques.
CO5: Document findings in a professional penetration testing report.

Covers cloud computing concepts, service models, deployment models, and virtualization technologies.

Course Outcome:

CO1: Understand cloud computing fundamentals, service models, and deployment models.
CO2: Understand virtualization technologies and their role in cloud computing.
CO3: Explore cloud storage, security, and resource management concepts.
CO4: Understand cloud service platforms such as AWS/Azure/GCP.
CO5: Analyze cloud computing applications and emerging trends.

Hands-on lab for deploying and managing applications on cloud platforms.

Course Outcome:

CO1: Create and configure virtual machines/instances on a cloud platform.
CO2: Deploy web applications using cloud services.
CO3: Configure cloud storage and database services.
CO4: Implement basic cloud security configurations.
CO5: Monitor and manage cloud resources using platform tools.

Evaluation of industrial/summer training undertaken by students to assess practical exposure gained.

Course Outcome:

CO1: Demonstrate understanding of the industrial/organizational environment.
CO2: Apply theoretical knowledge to practical, real-world tasks undertaken during training.
CO3: Present and document the training experience through a report and viva-voce.
CO4: Reflect on skills gained and their relevance to career development.

Develops logical reasoning, quantitative aptitude, and analytical problem-solving skills.

Course Outcome:

CO1: Apply logical reasoning techniques to solve analytical problems.
CO2: Solve quantitative aptitude problems involving numbers and arithmetic.
CO3: Apply data interpretation techniques to analyze given data sets.
CO4: Develop problem-solving strategies for competitive examinations.

Advanced module on entrepreneurship focusing on innovation, funding, and venture execution.

Course Outcome:

CO1: Understand advanced funding options and investment readiness for startups.
CO2: Apply innovation management techniques to venture development.
CO3: Understand legal and regulatory aspects of starting a business.
CO4: Develop a comprehensive venture execution plan.
CO5: Present a pitch incorporating financial and operational planning.

6TH SEMESTER SUBJECTS

Covers cryptographic algorithms, network security protocols, and mechanisms to secure data communication.

Course Outcome:

CO1: Understand fundamentals of network security and cryptographic principles.
CO2: Apply symmetric and asymmetric encryption algorithms for data security.
CO3: Understand hash functions, digital signatures, and message authentication.
CO4: Apply key management and public key infrastructure concepts.
CO5: Understand network security protocols such as SSL/TLS, IPSec, and firewalls.
CO6: Analyze security threats and countermeasures in networked systems.

Hands-on lab for implementing cryptographic algorithms and network security techniques.

Course Outcome:

CO1: Implement classical and modern encryption algorithms.
CO2: Implement hashing and digital signature techniques.
CO3: Configure firewalls and basic network security tools.
CO4: Analyze network traffic for security vulnerabilities.
CO5: Implement secure communication using SSL/TLS concepts.

Covers advanced data structures and their applications in efficient algorithm design.

Course Outcome:

CO1: Understand advanced tree structures such as AVL, B-trees, and Red-Black trees.
CO2: Implement advanced graph algorithms and their applications.
CO3: Understand heap structures and priority queue implementations.
CO4: Apply hashing techniques and collision resolution strategies.
CO5: Understand advanced string matching and pattern searching algorithms.
CO6: Analyze the performance trade-offs of advanced data structures.

Lab-based implementation of advanced data structures and their applications.

Course Outcome:

CO1: Implement balanced tree structures such as AVL and B-trees.
CO2: Implement heap-based priority queues.
CO3: Implement advanced graph algorithms programmatically.
CO4: Implement hashing techniques with collision handling.
CO5: Implement string matching algorithms.

Introduces the phases of compiler construction including lexical analysis, parsing, and code generation.

Course Outcome:

CO1: Understand the phases and structure of a compiler.
CO2: Design lexical analyzers using regular expressions and finite automata.
CO3: Design parsers using context-free grammars and parsing techniques.
CO4: Understand syntax-directed translation and intermediate code generation.
CO5: Understand code optimization and target code generation techniques.
CO6: Explore error detection and recovery mechanisms in compilers.

Covers data warehouse architecture, OLAP concepts, and data mining techniques for knowledge discovery.

Course Outcome:

CO1: Understand data warehouse architecture and multidimensional data models.
CO2: Apply OLAP operations for data analysis.
CO3: Understand data preprocessing techniques for data mining.
CO4: Apply classification and clustering algorithms for pattern discovery.
CO5: Understand association rule mining techniques.
CO6: Explore applications of data mining in real-world domains.

Introduces user interface and user experience design principles for creating effective digital products.

Course Outcome:

CO1: Understand fundamentals of UI/UX design and design thinking.
CO2: Apply wireframing and prototyping techniques.
CO3: Understand usability principles and user research methods.
CO4: Design responsive and accessible user interfaces.
CO5: Evaluate designs through usability testing.

Hands-on lab for creating wireframes, prototypes, and interactive designs using design tools.

Course Outcome:

CO1: Create wireframes and mockups using design tools.
CO2: Develop interactive prototypes for web/mobile applications.
CO3: Apply usability heuristics to evaluate designs.
CO4: Conduct basic user research and testing.
CO5: Design a complete UI/UX case study project.

Covers business intelligence concepts, data analytics, and reporting tools for decision-making.

Course Outcome:

CO1: Understand fundamentals of business intelligence and decision support systems.
CO2: Apply ETL processes for data integration.
CO3: Design dashboards and reports using BI tools.
CO4: Apply data visualization techniques for business insights.
CO5: Understand predictive analytics concepts in a business context.

Hands-on lab for building dashboards and reports using business intelligence tools.

Course Outcome:

CO1: Perform data extraction, transformation, and loading using BI tools.
CO2: Build interactive dashboards using visualization tools such as Power BI.
CO3: Design reports for business decision-making.
CO4: Apply data modeling techniques in a BI tool.
CO5: Present business insights derived from data analysis.

Builds advanced logical reasoning, quantitative aptitude, and analytical problem-solving skills.

Course Outcome:

CO1: Apply advanced logical reasoning techniques to complex problems.
CO2: Solve advanced quantitative aptitude problems.
CO3: Apply data sufficiency and interpretation techniques.
CO4: Develop strategies for competitive and placement examinations.

Provides students hands-on experience in identifying, designing, and initiating a substantial project applying learned concepts.

Course Outcome:

CO1: Identify a real-world problem and formulate project objectives.
CO2: Conduct literature review and requirement analysis for the project.
CO3: Design the system architecture and methodology for the project.
CO4: Develop an initial working prototype of the proposed solution.
CO5: Present and document the project progress through reports and reviews.

Focuses on developing communication, interpersonal, and professional workplace skills.

Course Outcome:

CO1: Develop effective verbal and written communication skills.
CO2: Apply interpersonal and teamwork skills in professional settings.
CO3: Understand workplace etiquette and professional ethics.
CO4: Develop resume writing and interview preparation skills.

7TH SEMESTER SUBJECTS

Introduces image processing fundamentals and computer vision techniques for visual data analysis.

Course Outcome:

CO1: Understand fundamentals of digital image processing and representation.
CO2: Apply image filtering, enhancement, and edge detection techniques.
CO3: Understand feature extraction and object detection techniques.
CO4: Apply image segmentation and classification techniques.
CO5: Understand deep learning approaches for computer vision tasks.
CO6: Explore real-world applications of computer vision.

Covers Internet of Things architecture, protocols, and applications for connected devices.

Course Outcome:

CO1: Understand IoT architecture, components, and protocols.
CO2: Apply sensor and actuator interfacing concepts.
CO3: Understand communication protocols used in IoT systems.
CO4: Apply cloud integration concepts for IoT data management.
CO5: Understand IoT security challenges and solutions.
CO6: Explore real-world IoT application domains.

Hands-on lab for building IoT-based projects using microcontrollers, sensors, and connectivity modules.

Course Outcome:

CO1: Interface sensors and actuators with microcontroller boards.
CO2: Implement basic IoT communication protocols.
CO3: Develop IoT applications with cloud data integration.
CO4: Implement basic IoT security measures.
CO5: Build and demonstrate a working IoT-based mini-project.

Introduces principles of designing scalable, reliable, and maintainable software systems.

Course Outcome:

CO1: Understand fundamentals of system design and design trade-offs.
CO2: Apply scalability concepts including load balancing and caching.
CO3: Design database and storage solutions for large-scale systems.
CO4: Understand microservices architecture and distributed system design.
CO5: Design fault-tolerant and highly available systems.
CO6: Apply system design principles to real-world case studies.

Introduces blockchain concepts, architecture, and applications in decentralized systems.

Course Outcome:

CO1: Understand fundamentals of blockchain technology and distributed ledgers.
CO2: Understand consensus mechanisms used in blockchain networks.
CO3: Apply smart contract concepts for decentralized applications.
CO4: Understand cryptocurrency and blockchain-based financial systems.
CO5: Explore blockchain applications beyond cryptocurrency.

Hands-on lab for developing and deploying blockchain-based applications and smart contracts.

Course Outcome:

CO1: Set up a basic blockchain network environment.
CO2: Develop and deploy smart contracts.
CO3: Build a simple decentralized application (DApp).
CO4: Implement basic cryptocurrency transaction simulations.
CO5: Evaluate blockchain applications for real-world use cases.

Introduces research methods, ethics, and technical writing skills for academic and applied research.

Course Outcome:

CO1: Understand fundamentals of research design and methodology.
CO2: Apply literature review and research problem formulation techniques.
CO3: Understand data collection and analysis methods for research.
CO4: Apply research ethics and plagiarism-avoidance practices.
CO5: Develop skills for writing and presenting research papers.

Covers agile methodologies, frameworks, and practices for iterative software development.

Course Outcome:

CO1: Understand agile principles and the agile manifesto.
CO2: Apply Scrum framework practices including sprints and ceremonies.
CO3: Apply Kanban and other agile project management techniques.
CO4: Understand agile estimation and planning techniques.
CO5: Apply continuous integration and delivery practices in agile projects.

Hands-on lab for practicing agile ceremonies and tools in a simulated project environment.

Course Outcome:

CO1: Plan and execute sprints using agile project management tools.
CO2: Conduct agile ceremonies such as stand-ups and retrospectives.
CO3: Create and manage a product backlog.
CO4: Apply agile estimation techniques to project tasks.
CO5: Demonstrate incremental delivery of a mini-project using agile practices.

Introduces ad-hoc network architectures, routing protocols, and wireless sensor network concepts.

Course Outcome:

CO1: Understand fundamentals of ad-hoc and wireless sensor networks.
CO2: Analyze routing protocols used in ad-hoc networks.
CO3: Understand energy-efficient design considerations in sensor networks.
CO4: Understand data aggregation and communication techniques in WSNs.
CO5: Explore security challenges and applications of WSNs.

Hands-on lab for simulating ad-hoc networks and wireless sensor network scenarios.

Course Outcome:

CO1: Simulate ad-hoc network topologies using networking simulators.
CO2: Implement and analyze routing protocols in simulation.
CO3: Simulate sensor node deployment and data collection scenarios.
CO4: Analyze network performance metrics in simulated WSN scenarios.
CO5: Evaluate energy efficiency in simulated sensor network models.

Continuation of the capstone project focusing on implementation, testing, and final deployment.

Course Outcome:

CO1: Implement the complete proposed system based on the earlier design.
CO2: Conduct testing and validation of the developed system.
CO3: Refine the solution based on evaluation and feedback.
CO4: Document the complete project methodology, results, and outcomes.
CO5: Present and defend the final project through a viva-voce/demonstration.

Focuses on advanced professional skills including leadership, career readiness, and workplace communication.

Course Outcome:

CO1: Develop advanced presentation and public speaking skills.
CO2: Apply leadership and team management concepts in professional settings.
CO3: Prepare for competitive job interviews and group discussions.
CO4: Understand corporate work culture and professional networking practices.

8TH SEMESTER SUBJECTS

Provides students with industry exposure through a structured training/internship period to apply academic knowledge in a professional environment.

Course Outcome:

CO1: Demonstrate understanding of industry practices, tools, and work culture.
CO2: Apply academic knowledge and skills to real-world industrial tasks.
CO3: Develop professional and technical skills relevant to the chosen domain.
CO4: Present the outcomes of industrial training through a report and viva-voce.

Admissions Open 2026-27

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B.Tech. Computer Science and Engineering (Lateral Entry)

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Course Curriculum

3RD SEMESTER SUBJECTS

Database Management System

Database Management System Lab

Data Structures and Algorithms

Data Structures and Algorithms Lab

Engineering Mathematics-III

Artificial Intelligence

Entrepreneurship Mindset-III

Frontend Technologies

Frontend Technologies Lab

Foundation of AI

Foundation of AI Lab

4TH SEMESTER SUBJECTS

Analysis & Design of Algorithms

Analysis & Design of Algorithms Lab

Computer Networks

Computer Networks Lab

Discrete Structures

Operating System

Operating System Lab

Computer Architecture and Organization

Web Technologies using MERN Stack

Web Technologies using MERN Stack Lab

Search Engine Optimization

Search Engine Optimization Lab

Entrepreneurship Mindset-IV

5TH SEMESTER SUBJECTS

Software Engineering

Theory of Computation

Java Programming

Java Programming Lab

Information Security and Cyber Laws

Ethical Hacking and Penetration Testing

Ethical Hacking and Penetration Testing Lab

Cloud Computing

Cloud Computing Lab

Summer Training-I Evaluation

Analytical Skills - I

Entrepreneurship Mindset- V

6TH SEMESTER SUBJECTS

Network Security and Cryptography

Network Security and Cryptography Lab

Advanced Data Structure

Advanced Data Structure Lab

Compiler Design

Data Warehousing and Data Mining

Admissions
Open 2026-27

Industry-oriented
learning

Modern campus
facilities

Academic excellence &
mentorship

Global opportunities &
placements