B.Tech. Computer Science and Engineering with Specialisation in Artificial Intelligence and Data Sciences

Fee Type	Amount
Programme Fees (per Semester)	₹85000
Examination Fees	₹3000

Slab	>=60% - 74.99%	>=75% - 89.99%	>=90% & Above
Fee	₹80000	₹75000	₹70000

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.

Covers advanced Python programming concepts including OOP, file handling, and modules for real-world application development.

Course Outcome:

CO1: Understand advanced Python programming constructs and best practices.
CO2: Apply object-oriented programming concepts using Python.
CO3: Implement file handling and exception handling in Python applications.
CO4: Use Python modules, packages, and libraries for application development.
CO5: Apply Python for data manipulation and automation tasks.

Hands-on lab for developing Python applications using advanced programming concepts.

Course Outcome:

CO1: Implement object-oriented Python programs.
CO2: Implement file handling and exception handling programs.
CO3: Develop applications using Python libraries and modules.
CO4: Implement automation scripts using Python.
CO5: Develop a mini-project using advanced Python concepts.

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.

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.

Introduces machine learning concepts and algorithms with hands-on implementation using Python.

Course Outcome:

CO1: Understand fundamentals of machine learning and types of learning.
CO2: Implement supervised learning algorithms such as regression and classification.
CO3: Implement unsupervised learning algorithms such as clustering.
CO4: Apply model evaluation and performance metrics.
CO5: Use Python libraries such as scikit-learn for machine learning tasks.
CO6: Apply machine learning techniques to solve real-world problems.

Hands-on lab for implementing machine learning algorithms using Python libraries.

Course Outcome:

CO1: Implement data preprocessing techniques using Python.
CO2: Implement supervised learning algorithms programmatically.
CO3: Implement unsupervised learning algorithms programmatically.
CO4: Evaluate machine learning models using standard metrics.
CO5: Develop a mini-project using machine learning techniques.

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.

Introduces data science concepts, data analysis workflows, and statistical techniques for extracting insights from data.

Course Outcome:

CO1: Understand the data science lifecycle and workflow.
CO2: Apply data cleaning and preprocessing techniques.
CO3: Apply exploratory data analysis and statistical techniques to datasets.
CO4: Apply data visualization techniques for effective communication of insights.
CO5: Understand basics of predictive modeling in data science.

Hands-on lab for data analysis and visualization using Python-based data science tools.

Course Outcome:

CO1: Perform data cleaning and preprocessing using Python libraries.
CO2: Perform exploratory data analysis on real-world datasets.
CO3: Create data visualizations using tools such as Matplotlib/Seaborn.
CO4: Apply basic statistical analysis techniques to datasets.
CO5: Develop a mini data analysis project.

Covers applied data science techniques including predictive modeling and real-world case studies.

Course Outcome:

CO1: Apply data science techniques to solve domain-specific problems.
CO2: Implement predictive models using real-world datasets.
CO3: Apply feature engineering techniques to improve model performance.
CO4: Evaluate and validate data science models using appropriate metrics.
CO5: Understand deployment considerations for data science solutions.
CO6: Analyze case studies of applied data science across industries.

Hands-on lab for building and deploying applied data science solutions.

Course Outcome:

CO1: Implement feature engineering and model-building pipelines.
CO2: Build predictive models on real-world datasets.
CO3: Evaluate model performance using standard validation techniques.
CO4: Apply data science solutions to a case-study based problem.
CO5: Present a complete applied data science mini-project.

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 machine learning concepts, algorithms, and model evaluation techniques for predictive analytics.

Course Outcome:

CO1: Understand fundamentals of machine learning and types of learning paradigms.
CO2: Apply supervised learning algorithms for regression and classification tasks.
CO3: Apply unsupervised learning algorithms for clustering and pattern discovery.
CO4: Understand model evaluation, validation, and performance metrics.
CO5: Apply ensemble learning and dimensionality reduction techniques.
CO6: Explore real-world applications of machine learning across domains.

Hands-on lab for implementing machine learning algorithms using Python-based tools and libraries.

Course Outcome:

CO1: Implement data preprocessing and feature engineering techniques.
CO2: Implement supervised learning algorithms programmatically.
CO3: Implement unsupervised learning algorithms programmatically.
CO4: Evaluate machine learning models using standard validation techniques.
CO5: Develop a mini-project applying machine learning to a real-world dataset.

Covers big data concepts, distributed processing frameworks, and data pipeline design for large-scale data systems.

Course Outcome:

CO1: Understand fundamentals of big data and its characteristics (volume, velocity, variety).
CO2: Understand distributed computing frameworks such as Hadoop and Spark.
CO3: Design data pipelines for ingestion, storage, and processing of big data.
CO4: Apply big data processing techniques for batch and stream data.
CO5: Understand NoSQL databases and their role in big data systems.
CO6: Explore big data applications and emerging trends in data engineering.

Hands-on lab for implementing big data processing pipelines using distributed computing tools.

Course Outcome:

CO1: Set up and configure a basic distributed computing environment.
CO2: Implement data ingestion and storage using big data tools.
CO3: Process large datasets using frameworks such as Spark/Hadoop.
CO4: Implement basic stream processing tasks.
CO5: Develop a mini big data processing project.

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 deep learning concepts, architectures, and training techniques for building intelligent systems.

Course Outcome:

CO1: Understand fundamentals of neural networks and deep learning architectures.
CO2: Apply forward and backward propagation techniques for training neural networks.
CO3: Implement convolutional neural networks for image-related tasks.
CO4: Implement recurrent neural networks for sequential data processing.
CO5: Apply regularization and optimization techniques to improve model performance.
CO6: Explore applications of deep learning across various domains.

Hands-on lab for building and training deep learning models using standard frameworks.

Course Outcome:

CO1: Implement basic neural network models using deep learning frameworks.
CO2: Implement convolutional neural networks for image classification tasks.
CO3: Implement recurrent neural networks for sequence data.
CO4: Apply hyperparameter tuning and optimization techniques.
CO5: Develop a mini-project using a deep learning model.

Covers advanced neural network architectures and their applications in complex problem-solving.

Course Outcome:

CO1: Understand advanced neural network architectures beyond basic deep learning models.
CO2: Apply generative models such as GANs and autoencoders.
CO3: Understand attention mechanisms and transformer-based architectures.
CO4: Apply transfer learning techniques to real-world problems.
CO5: Evaluate and fine-tune advanced neural network models.
CO6: Explore cutting-edge applications of neural networks in AI research.

Hands-on lab for implementing advanced neural network architectures and applications.

Course Outcome:

CO1: Implement generative models such as autoencoders and GANs.
CO2: Implement transformer-based or attention-based models.
CO3: Apply transfer learning to pretrained neural network models.
CO4: Fine-tune and evaluate advanced neural network architectures.
CO5: Develop a mini-project using an advanced neural network technique.

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.

Introduces NLP concepts, text processing techniques, and language modeling approaches.

Course Outcome:

CO1: Understand fundamentals of natural language processing and text representation.
CO2: Apply text preprocessing techniques such as tokenization, stemming, and lemmatization.
CO3: Understand syntactic and semantic analysis techniques in NLP.
CO4: Apply statistical and neural language modeling techniques.
CO5: Implement NLP applications such as sentiment analysis and text classification.
CO6: Explore real-world applications of NLP across domains.

Hands-on lab for implementing NLP techniques using Python-based libraries.

Course Outcome:

CO1: Implement text preprocessing pipelines using NLP libraries.
CO2: Implement part-of-speech tagging and named entity recognition.
CO3: Build text classification and sentiment analysis models.
CO4: Implement basic language modeling techniques.
CO5: Develop a mini NLP application project.

Introduces generative AI concepts, large language models, and prompt engineering techniques.

Course Outcome:

CO1: Understand fundamentals of generative AI and generative model architectures.
CO2: Understand the architecture and working of large language models.
CO3: Apply prompt engineering techniques for effective LLM interaction.
CO4: Understand fine-tuning and adaptation techniques for LLMs.
CO5: Explore ethical considerations and limitations of generative AI.
CO6: Apply generative AI techniques to real-world use cases.

Hands-on lab for building applications using generative AI models and large language models.

Course Outcome:

CO1: Interact with and evaluate outputs of pretrained large language models.
CO2: Apply prompt engineering techniques to solve specific tasks.
CO3: Build a simple application using an LLM API.
CO4: Implement basic fine-tuning or adaptation of a generative model.
CO5: Develop a mini-project using generative AI/LLM techniques.

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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Course Curriculum

3RD SEMESTER SUBJECTS

Database Management System

Database Management System Lab

Data Structures and Algorithms

Data Structures and Algorithms Lab

Engineering Mathematics-III

Advanced Python Programming

Advanced Python Programming Lab

Entrepreneurship Mindset-III

Foundation of AI

Foundation of AI Lab

Machine Learning with Python

Machine Learning with Python 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

Data Science

Data Science Lab

Applied Data Science

Applied Data Science Lab

Entrepreneurship Mindset-IV

5TH SEMESTER SUBJECTS

Software Engineering

Theory of Computation

Java Programming

Java Programming Lab

Information Security and Cyber Laws

Machine Learning

Machine Learning Lab

Big Data Engineering

Big Data Engineering 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

Admissions
Open 2026-27

Industry-oriented
learning

Modern campus
facilities

Academic excellence &
mentorship

Global opportunities &
placements