MCA With IBM Specialization (Data Sciences & AI)

Fee Type	Amount
Programme Fees (per Semester)	₹70000
Examination Fees	₹3000
International Fee (USD)	$3300

Course Curriculum

1ST SEMESTER SUBJECTS

Covers the software development life cycle, requirements engineering, design and testing methodologies, software quality assurance, and project planning, estimation, scheduling, and risk management techniques used to manage software projects effectively.

Course Outcome:

CO1: Understand the fundamental concepts of software engineering, including software process models and their applicability to different types of projects.
CO2: Analyze and document software requirements effectively, and prepare a Software Requirements Specification (SRS) using appropriate analysis tools.
CO3: Apply software design principles and UML modeling techniques to develop architectural and detailed designs for software systems.
CO4: Apply various software testing techniques and strategies to ensure software quality and reliability.
CO5: Apply project management techniques such as estimation, scheduling, and risk management to plan and manage software projects effectively.
CO6: Understand software configuration management practices and apply appropriate maintenance strategies throughout the software lifecycle.

Explores advanced networking concepts including network architectures, routing and switching protocols, network security, wireless and mobile networks, and emerging technologies such as SDN and cloud-based networking.

Course Outcome:

CO1: Understand the foundational principles of network security, including the CIA triad, types of attacks, and cryptographic mechanisms used to secure network communications.
CO2: Analyze and apply cryptographic algorithms and authentication protocols such as AES, RSA, Diffie-Hellman, and Kerberos to design secure communication systems.
CO3: Evaluate and implement network security protocols including IPSec, SSL/TLS, VPN, and SSH to ensure secure data transmission across networks.
CO4: Design and configure firewall architectures, IDS/IPS systems, and access control mechanisms to protect network infrastructure from unauthorized access.
CO5: Identify, analyze, and mitigate advanced cyber threats such as DDoS, APT, malware, and web-based attacks using appropriate countermeasures and security frameworks.
CO6: Explore and apply emerging network security concepts including wireless security, cloud security, IoT security, and network forensics to address modern cybersecurity challenges.

Focuses on advanced relational database concepts including normalization, transaction management, concurrency control, query optimization, distributed databases, and administration of enterprise-grade database systems.

Course Outcome:

CO1: Review and apply core relational database concepts including relational algebra, normalization, and SQL to design and implement well- structured relational schemas.
CO2: Write and optimize advanced SQL queries using stored procedures, triggers, window functions, and indexing strategies to improve database performance and efficiency.
CO3: Analyze and implement transaction management and concurrency control mechanisms to ensure data consistency, integrity, and isolation in multi-user database environments.
CO4: Apply database recovery techniques and security mechanisms to protect database systems from failures, unauthorized access, and data breaches.
CO5: Understand and implement distributed database concepts including fragmentation, replication, and distributed transaction management to build scalable and fault-tolerant systems.
CO6: Explore advanced RDBMS topics such as object-relational models, temporal and spatial databases, data warehousing, and integration with modern Big Data technologies to address complex data management challenges.

Provides hands-on practice in designing, implementing, and querying relational databases, writing complex SQL queries, stored procedures, triggers, and performing database tuning and administration tasks.

Course Outcome:

CO1: Design and develop conceptual and relational database models using ER diagrams, normalization techniques, and integrity constraints.
CO2: Create, modify, and manage database objects using SQL DDL, DML, and DCL commands.
CO3: Apply SQL queries, built-in functions, nested queries, joins, and constraints to retrieve and manipulate data efficiently.
CO4: Develop advanced database programs using cursors, stored procedures, functions, triggers, and embedded SQL.
CO5: Implement database administration tasks including user management, role creation, privilege assignment, and data import/export operations.
CO6: Design and implement complete database applications by integrating database design, programming, querying, and administration techniques.

Covers advanced data structures such as trees, graphs, and hashing along with algorithm design paradigms including divide-and-conquer, dynamic programming, and greedy methods, implemented and analyzed using Python.

Course Outcome:

CO1: Analyze and evaluate the time and space complexity of algorithms using asymptotic notations and apply Python's OOP features to implement efficient Abstract Data Types.
CO2: Design and implement advanced linear data structures such as skip lists, monotonic stacks, priority queues, and string structures to solve complex computational problems efficiently.
CO3: Implement and apply advanced tree data structures including AVL trees, segment trees, tries, and heaps to solve problems related to searching, sorting, and hierarchical data management.
CO4: Apply graph algorithms including shortest path, minimum spanning tree, topological sort, network flow, and strongly connected components to model and solve real-world network problems using Python.
CO5: Design solutions to complex computational problems using algorithm design paradigms such as dynamic programming, greedy algorithms, backtracking, and branch and bound techniques.
CO6: Explore and apply advanced algorithmic concepts including hashing, string matching, computational geometry, NP-completeness, and Python optimization libraries to solve modern and large-scale computational challenges.

Offers practical implementation of advanced data structures and algorithms in Python, reinforcing algorithmic problem-solving skills and performance analysis through coding exercises.

Course Outcome:

CO1: Apply Python programming concepts to implement fundamental and advanced data structures.
CO2: Develop and analyze algorithms for searching, sorting, and data organization problems.
CO3: Implement linear and non-linear data structures such as linked lists, trees, heaps, and graphs.
CO4: Design solutions using algorithmic paradigms including recursion, divide-and-conquer, greedy, dynamic programming, and backtracking.
CO5: Analyze algorithm efficiency using asymptotic notations and compare performance of different approaches.
CO6: Develop optimized Python-based solutions for real-world computational problems using advanced data structures and algorithms.

Introduces contemporary artificial intelligence techniques and data analysis methods, including machine learning fundamentals, data preprocessing, exploratory data analysis, and their applications in solving real-world problems.

Course Outcome:

CO1: Understand the foundational concepts of Artificial Intelligence and Data Analysis, and apply Python libraries to collect, explore, and visualize real-world datasets effectively.
CO2: Apply data preprocessing and exploratory data analysis techniques including feature engineering, dimensionality reduction, and statistical analysis to prepare high-quality datasets for modeling.
CO3: Implement and evaluate supervised and unsupervised machine learning algorithms using Scikit-learn to solve classification, regression, and clustering problems on real-world data.
CO4: Design and train deep learning models including CNN, RNN, LSTM, and GANs using TensorFlow and PyTorch for tasks such as image recognition, sequence prediction, and data generation.
CO5: Apply Natural Language Processing and Computer Vision techniques using transformer-based models, LLMs, and OpenCV to build intelligent text and image understanding applications.
CO6: Explore modern AI trends including Big Data integration, MLOps, Explainable AI, Reinforcement Learning, Edge AI, and ethical considerations to design responsible and scalable AI systems.

An elective covering foundational concepts of artificial intelligence, including intelligent agents, search strategies, knowledge representation, reasoning, and an introduction to machine learning approaches.

Course Outcome:

CO1: Explain the history, evolution, concepts, and real-world applications of Artificial Intelligence.
CO2: Apply Python programming and popular AI libraries for solving basic AI-related problems.
CO3: Demonstrate the working of Machine Learning algorithms for prediction and classification tasks.
CO4: Describe the fundamentals of Neural Networks, Deep Learning, Computer Vision, and Natural Language Processing.
CO5: Utilize Generative AI tools responsibly while understanding ethical AI principles and data privacy.

Hands-on elective lab for implementing foundational AI algorithms and techniques covered in AI Concepts, including search, reasoning, and basic machine learning models.

Course Outcome:

CO1: Understand the principle and objective of the experiment.
CO2: Perform the experimental procedure using the appropriate equipment and safety practices.
CO3: Record, analyze, and interpret the experimental data accurately.
CO4: Evaluate the results by comparing them with theoretical or expected values.
CO5: Develop problem-solving and technical skills related to the experiment and its practical.

An elective introducing the R programming language for statistical computing and data analysis, covering data structures, data manipulation, visualization, and statistical modeling in R.

Course Outcome:

CO1: Explain the fundamentals of R programming, including data types, operators, variables, and control structures.
CO2: Apply R functions, vectors, matrices, lists, and data frames to solve computational problems.
CO3: Analyze and manipulate datasets using R programming techniques.
CO4: Create suitable visualizations in R for effective interpretation of data.
CO5: Develop R programs for basic statistical analysis and data-driven problem solving.

Hands-on elective lab for applying R programming to real datasets, covering data import/cleaning, visualization, and basic statistical analysis using R.

Course Outcome:

CO1: Develop basic R programs using variables, data types, operators, expressions, and control structures.
CO2: Apply R programming concepts such as vectors, matrices, arrays, lists, and data frames to solve computational problems.
CO3: Implement functions and perform data manipulation using R for handling and processing datasets.
CO4: Analyze datasets using basic statistical techniques available in R.
CO5: Create suitable charts and graphs in R to visualize and interpret data effectively.
CO6: Develop simple R-based solutions for real-world data analysis problems using appropriate programming and analytical techniques. MCA(AI) Batch-2026

2ND SEMESTER SUBJECTS

Covers core machine learning concepts including supervised and unsupervised learning, regression, classification, clustering, and model evaluation, implemented using Python and popular ML libraries.

Course Outcome:

CO1: Explain fundamental concepts and principles of machine learning and its applications.
CO2: Apply Python programming and machine learning libraries for data preprocessing and analysis.
CO3: Implement supervised learning techniques such as regression and classification for solving predictive problems.
CO4: Apply unsupervised learning techniques such as clustering to analyze datasets.
CO5: Evaluate and compare machine learning models using appropriate performance measures.
CO6: Develop suitable machine learning solutions for real-world problems using Python.

Hands-on lab for implementing machine learning algorithms in Python, covering data preprocessing, model building, training, and evaluation on real-world datasets.

Course Outcome:

CO1: Implement basic machine learning programs using Python and appropriate libraries.
CO2: Perform data cleaning, preprocessing, feature selection, and transformation on datasets.
CO3: Build and train supervised learning models for regression and classification problems.
CO4: Implement clustering algorithms and analyze patterns in datasets.
CO5: Evaluate machine learning models using suitable performance metrics and visualization techniques.
CO6: Develop and test machine learning solutions using real-world datasets.

Introduces data warehousing concepts including ETL processes, OLAP, dimensional modeling, and business intelligence tools used for data-driven decision making and reporting.

Course Outcome:

CO1: Explain the concepts, architecture, and components of data warehouses and business intelligence.
CO2: Apply ETL techniques for extracting, transforming, and loading data into a data warehouse.
CO3: Design dimensional models using facts, dimensions, star schemas, and snowflake schemas.
CO4: Apply OLAP operations for multidimensional data analysis.
CO5: Use business intelligence techniques and tools to generate meaningful reports and insights.
CO6: Analyze organizational data to support data-driven decision making.

Explores soft computing techniques including fuzzy logic, artificial neural networks, genetic algorithms, and hybrid systems used to solve complex, real-world problems involving uncertainty and imprecision.

Course Outcome:

CO1: Explain the fundamental concepts and principles of soft computing techniques.
CO2: Apply fuzzy logic concepts to solve problems involving uncertainty and imprecision.
CO3: Design and implement basic artificial neural network models for problem solving.
CO4: Apply genetic algorithms to optimization and search problems.
CO5: Compare different soft computing techniques based on their suitability for specific problems.
CO6: Develop suitable soft computing solutions for complex real-world problems.

Covers principles of research methodology including literature review, research design, data collection and analysis techniques, academic writing, and preparation of a formal project proposal.

Course Outcome:

CO1: Explain the fundamental concepts, types, and processes of research.
CO2: Conduct systematic literature reviews using appropriate academic and research resources.
CO3: Formulate research problems, objectives, research questions, and hypotheses.
CO4: Apply appropriate research methods for data collection, analysis, and interpretation.
CO5: Prepare academic and technical documents using appropriate research writing and citation practices.
CO6: Develop and present a structured research/project proposal based on an identified problem.

Covers formal languages, automata theory, regular expressions, context-free grammars, Turing machines, and computability and complexity theory, forming the theoretical foundation of computer science.

Course Outcome:

CO1: Explain the fundamental concepts of formal languages, grammars, and automata theory.
CO2: Construct and analyze finite automata for recognizing regular languages.
CO3: Apply regular expressions and context-free grammars to represent and analyze formal languages.
CO4: Construct and analyze pushdown automata for context-free languages.
CO5: Explain the structure and computational capabilities of Turing machines.
CO6: Analyze basic concepts of computability, decidability, and computational complexity.

Introduces the fundamental concepts of machine learning and data science, including data collection, preprocessing, exploratory data analysis, statistical techniques, supervised and unsupervised learning, model evaluation, and data visualization. The course focuses on applying Python and relevant libraries to analyze data, build predictive models, and derive meaningful insights from real-world datasets.

Course Outcome:

CO1: Explain the fundamental concepts of data science, machine learning, and their applications.
CO2: Apply data preprocessing and exploratory data analysis techniques to prepare datasets for analysis.
CO3: Implement supervised learning techniques for solving regression and classification problems.
CO4: Apply unsupervised learning techniques to identify patterns and clusters in datasets.
CO5: Evaluate machine learning models using appropriate performance measures and visualization techniques.
CO6: Develop data-driven solutions to real-world problems using suitable machine learning and data science techniques.

Provides hands-on experience in applying machine learning and data science techniques using Python. The laboratory covers data preprocessing, exploratory data analysis, visualization, implementation of supervised and unsupervised learning algorithms, model training, and performance evaluation using real-world datasets.

Course Outcome:

CO1: Implement basic data science and machine learning programs using Python and relevant libraries.
CO2: Perform data cleaning, preprocessing, transformation, and exploratory analysis on datasets.
CO3: Create suitable visualizations to analyze and communicate data patterns and insights.
CO4: Implement supervised learning algorithms for regression and classification problems.
CO5: Implement unsupervised learning algorithms for clustering and pattern identification.
CO6: Build, evaluate, and interpret machine learning models using real-world datasets.

An elective introducing blockchain fundamentals including distributed ledgers, consensus mechanisms, cryptographic principles, smart contracts, and applications of blockchain across various industries.

Course Outcome:

CO1: Explain the fundamental concepts, architecture, and components of blockchain technology.
CO2: Describe the role of cryptography and distributed ledgers in blockchain systems.
CO3: Analyze different blockchain consensus mechanisms and their applications.
CO4: Explain the structure, functionality, and applications of smart contracts.
CO5: Evaluate blockchain applications across different industries and use cases.
CO6: Design suitable blockchain-based solutions for real-world applications.

Practical elective lab for building and deploying blockchain applications, including writing and testing smart contracts and working with blockchain development platforms.

Course Outcome:

CO1: Configure and work with basic blockchain development environments and platforms.
CO2: Implement fundamental blockchain concepts using suitable development tools.
CO3: Develop, test, and deploy basic smart contracts.
CO4: Apply cryptographic and consensus concepts in blockchain-based applications.
CO5: Develop simple decentralized applications using blockchain technologies.
CO6: Test and evaluate blockchain applications for appropriate real-world use cases.

An elective covering fundamentals of computer graphics including rendering, transformations, modeling, and data visualization techniques used to represent and interpret complex data visually.

Course Outcome:

CO1: Explain the fundamental concepts and principles of computer graphics and visualization.
CO2: Apply geometric transformations such as translation, rotation, scaling, and reflection.
CO3: Explain and apply basic rendering, modeling, and viewing techniques.
CO4: Apply computer graphics algorithms for generating and manipulating graphical objects.
CO5: Apply visualization techniques to represent and interpret complex data effectively.
CO6: Analyze and select appropriate graphics and visualization techniques for real-world applications.

Hands-on elective lab for implementing computer graphics and visualization techniques, including rendering, transformations, and building visual representations of data.

Course Outcome:

CO1: Implement basic computer graphics programs using appropriate programming tools.
CO2: Implement two-dimensional and three-dimensional geometric transformations.
CO3: Develop graphical objects using suitable rendering and modeling techniques.
CO4: Implement basic graphics algorithms for drawing and manipulating graphical objects.
CO5: Create visual representations of data using appropriate visualization techniques.
CO6: Develop and demonstrate simple computer graphics and visualization applications. MCA(AI) Batch-2026

3RD SEMESTER SUBJECTS

This course covers the fundamentals of digital image processing including image acquisition, sampling, quantization, image enhancement, restoration, segmentation, morphological processing, image compression, and object recognition techniques used in intelligent vision systems.

Course Outcome:

CO1: Understand fundamentals of digital image formation, sampling, and quantization.
CO2: Apply spatial and frequency domain techniques for image enhancement and filtering.
CO3: Implement image restoration techniques to remove noise and degradation.
CO4: Apply segmentation and morphological operations to extract meaningful image regions.
CO5: Understand image compression techniques for efficient storage and transmission.
CO6: Apply feature extraction and pattern recognition techniques for object detection and image classification.

Provides hands-on practice in implementing image processing algorithms including image enhancement, filtering, segmentation, morphological operations, and object detection using Python-based image processing libraries.

Course Outcome:

CO1: Implement image acquisition, reading, and basic manipulation operations using Python libraries.
CO2: Apply spatial domain enhancement techniques such as histogram equalization and spatial filtering.
CO3: Implement frequency domain filtering using Fourier transform techniques.
CO4: Apply noise removal and image restoration techniques on degraded images.
CO5: Implement image segmentation and edge detection algorithms.
CO6: Develop a complete image processing application integrating enhancement, segmentation, and feature extraction.

Introduces cloud computing fundamentals and MLOps practices for deploying, monitoring, and managing machine learning models in production, covering cloud service models, containerization, CI/CD pipelines, model versioning, and monitoring tools.

Course Outcome:

CO1: Understand cloud computing service models (IaaS, PaaS, SaaS) and deployment models.
CO2: Apply containerization technologies such as Docker to package machine learning applications.
CO3: Design CI/CD pipelines for automated training, testing, and deployment of ML models.
CO4: Implement model versioning and experiment tracking using MLOps tools.
CO5: Deploy machine learning models on cloud platforms and monitor their performance in production.
CO6: Understand scalability, security, and cost optimization practices for cloud-based ML systems.

Hands-on lab for deploying machine learning models using cloud platforms and MLOps tools, covering containerization, pipeline automation, model deployment, and monitoring.

Course Outcome:

CO1: Set up and configure a cloud environment for machine learning workloads.
CO2: Containerize machine learning applications using Docker.
CO3: Build automated CI/CD pipelines for model training and deployment.
CO4: Deploy trained models as REST APIs on cloud platforms.
CO5: Implement model monitoring and logging for deployed ML services.
CO6: Develop an end-to-end MLOps pipeline integrating version control, testing, deployment, and monitoring.

Covers the fundamentals of big data processing frameworks including Hadoop ecosystem components (HDFS, MapReduce, YARN) and Apache Spark for distributed data processing, along with big data storage and processing techniques for large-scale datasets.

Course Outcome:

CO1: Understand big data characteristics and the Hadoop ecosystem architecture.
CO2: Apply HDFS for distributed storage and MapReduce programming for data processing.
CO3: Understand Spark architecture including RDDs, DataFrames, and Spark SQL.
CO4: Apply Spark for batch processing of large-scale datasets.
CO5: Implement Spark Streaming for real-time data processing.
CO6: Design big data processing pipelines integrating Hadoop and Spark components.

Hands-on lab for implementing distributed data processing solutions using Hadoop and Spark, covering HDFS operations, MapReduce programming, Spark DataFrames, and Spark SQL queries.

Course Outcome:

CO1: Perform HDFS file operations and configure a Hadoop cluster environment.
CO2: Write and execute MapReduce programs for data processing tasks.
CO3: Implement data transformations using Spark RDDs and DataFrames.
CO4: Perform data analysis using Spark SQL queries.
CO5: Implement a real-time data processing application using Spark Streaming.
CO6: Develop an end-to-end big data analytics pipeline using Hadoop and Spark.

Provides comprehensive coverage of artificial neural networks and deep learning architectures including feedforward networks, convolutional neural networks, recurrent neural networks, and modern deep learning techniques for solving complex real-world problems.

Course Outcome:

CO1: Understand the fundamentals of artificial neural networks and backpropagation learning.
CO2: Design and train feedforward neural networks for classification and regression tasks.
CO3: Apply convolutional neural networks for image recognition and computer vision tasks.
CO4: Apply recurrent neural networks and LSTM architectures for sequential data processing.
CO5: Understand regularization, optimization, and hyperparameter tuning techniques for deep learning models.
CO6: Apply advanced deep learning architectures such as autoencoders and transformers to solve real-world problems.

Evaluates the summer internship undertaken by students after Semester II, assessing the practical exposure gained in an industry or research environment through a report and presentation before the school committee.

Course Outcome:

CO1: Demonstrate the practical skills and industry exposure gained during the summer internship.
CO2: Prepare a comprehensive internship report documenting the work undertaken.
CO3: Present and defend the internship work before the evaluation committee.

Covers statistical and machine learning techniques for predictive modeling including regression analysis, time series forecasting, classification techniques, and model evaluation methods used for data-driven decision-making.

Course Outcome:

CO1: Understand the fundamentals of predictive analytics and the predictive modeling lifecycle.
CO2: Apply regression techniques for continuous outcome prediction.
CO3: Apply classification techniques for categorical outcome prediction.
CO4: Implement time series forecasting techniques for trend and seasonality analysis.
CO5: Evaluate predictive models using appropriate performance metrics.
CO6: Apply predictive analytics techniques to solve real-world business problems.

Hands-on lab for implementing predictive modeling techniques using Python, covering regression, classification, time series forecasting, and model evaluation.

Course Outcome:

CO1: Implement data preprocessing techniques for predictive modeling.
CO2: Build and evaluate regression models for continuous prediction tasks.
CO3: Build and evaluate classification models for categorical prediction tasks.
CO4: Implement time series forecasting models using Python libraries.
CO5: Apply model evaluation and validation techniques to assess model performance.
CO6: Develop a complete predictive analytics application to solve a real-world problem.

Involves the design and development of a minor project applying AI and data science concepts learned throughout the programme, culminating in a working prototype and project report.

Course Outcome:

CO1: Identify a real-world problem suitable for an AI/data science-based solution.
CO2: Design and develop a working prototype applying appropriate techniques and tools.
CO3: Document and present the project work through a report and demonstration. MCA(AI) Batch-2026

3rd Semester – Basket-III (Discipline Elective-III)

Introduces IoT architecture, sensors, actuators, communication protocols, and robotics fundamentals, covering the integration of IoT devices with AI techniques for building smart and autonomous systems.

Course Outcome:

CO1: Understand IoT architecture, protocols, and sensor-actuator systems.
CO2: Apply communication protocols for IoT device connectivity.
CO3: Understand fundamentals of robotics including kinematics and control systems.
CO4: Apply AI techniques for building intelligent IoT and robotic systems.
CO5: Design an IoT-based system for real-world monitoring or automation applications.
CO6: Analyze security and privacy challenges in IoT and robotic systems.

Hands-on lab for building IoT and robotics applications using microcontrollers, sensors, actuators, and AI integration.

Course Outcome:

CO1: Set up and program microcontroller-based IoT development boards.
CO2: Interface sensors and actuators with IoT devices.
CO3: Implement communication protocols for IoT data transmission.
CO4: Build a basic robotic system with sensor-based control.
CO5: Integrate AI-based decision-making into an IoT/robotics application.
CO6: Develop a complete IoT/robotics prototype for a real-world use case.

Covers the fundamentals of data science including data collection, data wrangling, exploratory data analysis, statistical modeling, and data visualization techniques for extracting insights from data.

Course Outcome:

CO1: Understand the data science process and lifecycle.
CO2: Apply data collection and data wrangling techniques to prepare data for analysis.
CO3: Perform exploratory data analysis to identify patterns and trends in data.
CO4: Apply statistical modeling techniques for data analysis.
CO5: Apply data visualization techniques to communicate insights effectively.
CO6: Apply data science techniques to solve real-world analytical problems.

Hands-on lab applying data science techniques using Python, covering data wrangling, exploratory data analysis, statistical modeling, and visualization.

Course Outcome:

CO1: Perform data cleaning and preprocessing using Python libraries.
CO2: Conduct exploratory data analysis using statistical and visualization techniques.
CO3: Apply statistical modeling techniques to analyze datasets.
CO4: Build data visualization dashboards to communicate insights.
CO5: Apply hypothesis testing techniques to validate analytical findings.
CO6: Develop a complete data analysis project applying the full data science pipeline. MCA(AI) Batch-2026

4TH SEMESTER SUBJECTS

Involves the design, development, and implementation of a comprehensive major project addressing a real-world AI/data science problem, applying the complete knowledge and skills gained throughout the programme, culminating in a dissertation report and final viva presentation.

Course Outcome:

CO1: Identify and formulate a significant real-world problem suitable for an AI/data science-based solution.
CO2: Design a comprehensive system architecture and methodology to address the identified problem.
CO3: Develop, implement, and test a complete working solution applying appropriate AI/data science techniques and tools.
CO4: Evaluate the developed solution using appropriate performance metrics and validation techniques.
CO5: Document the project work in a comprehensive project report following academic and technical writing standards.
CO6: Present and defend the project work before an evaluation committee, demonstrating professional communication skills.

Provides students with extended industry exposure through a structured industrial training program, enabling them to apply their academic knowledge to real-world organizational problems, develop professional skills, and gain practical experience in an industry environment.

Course Outcome:

CO1: Apply academic knowledge and skills to solve real-world problems in an industrial setting.
CO2: Demonstrate professional and workplace skills including teamwork, communication, and time management.
CO3: Document the industrial training experience through a comprehensive training report.
CO4: Present and defend the industrial training work before the evaluation committee.

Admissions Open 2026-27

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Program Fee Details

Course Curriculum

1ST SEMESTER SUBJECTS

Software Engineering and Project Management

Advance Computer Networking

Advanced RDBMS

Advanced RDBMS Lab

Advanced Data Structures and Algorithms using Python

Advanced Data Structures and Algorithms using Python Lab

Modern AI and Data Analysis

AI Concepts

AI Concepts Lab

R Programming

R Programming Lab

2ND SEMESTER SUBJECTS

Machine Learning using Python

Machine Learning using Python Lab

Data Warehouse and Business Intelligence

Soft Computing

Research Methodology & Project Proposal

Theory of Computation

Machine Learning & Data Science

Machine Learning & Data Science lab

Blockchain Technologies

Blockchain Technologies Lab

Computer Graphics and Visualization

Computer Graphics and Visualization Lab

3RD SEMESTER SUBJECTS

Digital Image Processing

Digital Image Processing Lab

Cloud & MLOps Platforms

Cloud & MLOps Lab

Big Data Technologies (Hadoop & Spark)

Big Data Analytics (Hadoop & Spark) Lab

Deep Learning & Neural Networks

Summer Training Evaluation

Predictive Analytics

Predictive Analytics Lab

Minor Project

3rd Semester – Basket-III (Discipline Elective-III)

IOT and Robotics

IOT and Robotics Lab

Data Science

Data Science Lab

4TH SEMESTER SUBJECTS

Major Project

Industrial Training

Program Outcomes & Features

Program Outcomes

Admissions
Open 2026-27

Industry-oriented
learning

Modern campus
facilities

Academic excellence &
mentorship

Global opportunities &
placements