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Bachelor of Computer Application with Specialisation in Data Science and Artificial Intelligence (IBM)

program-details

As there is an enormous amount of data and to handle the data is one of the challenging tasks of various IT sectors. So, the Data Analytics and Artificial Intelligence has become an hour of need of today’s society. This course will help the students to become the emerging Data Scientists of the modern era.

Industry Immersion

MAJOR COURSES OFFERED

  • Python + Clean Coding
  • Data Visualization
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Predictive Analysis
  • NoSQL
  • Devops
  • Data Sciences
  • Big Data Fundamentals
  • BlockChain Technology

eligibility criteria

Passing of 10+2 or its equivalent examination in any stream conducted by a recognized Board / University / Council. 
OR
Having passed Matriculation examination and have also passed three-year Diploma in any Trade from Punjab State Board of Technical Education & Industrial Training, Chandigarh or such Examination from any other recognized State Board of Technical Education, or Sant Longowal Institute of Engineering & Technology, Longowal

Duration

3 Years

related program

Curriculum

1ST SEMESTER SUBJECTS

This course introduces the fundamentals of C programming, including program structure, algorithms, flowcharts, data types, operators, input/output, control statements, functions, recursion, arrays, and strings enabling students to develop structured and efficient programs and strengthen their problem-solving skills.
Course Outcome:
CO1: Apply C programming fundamentals to develop simple programs.
CO2: Use operators and control structures to solve programming problems.
CO3: Develop structured programs using functions, arrays, and strings.
CO4: Apply pointers for memory management and data manipulation.
CO5: Design programs using structures, unions, and user-defined data types.
CO6: Perform file operations for data storage and retrieval in C.

This practical course provides hands-on experience in C programming, covering basic programs, type casting, operators, control statements, loops, arrays, functions, strings, pointers, structures, and unions enabling students to develop, test, and implement C programs for solving real-world computational problems.
Course Outcome:
CO1: Apply the fundamental syntax, structure, and programming constructs of a programming language to develop basic programs.
CO2: Develop and debug programs to enhance logical thinking and problem-solving abilities.
CO3: Implement decision-making and iterative control structures to solve practical programming problems.
CO4: Design modular and reusable programs using functions and procedural decomposition techniques.
CO5: Create and manipulate arrays, structures, and unions to efficiently organize and process data.
CO6: Perform file handling operations for storing, retrieving, and managing data in programs.

This course provides a comprehensive understanding of computer fundamentals, hardware, software, memory, operating systems, and input/output devices, along with modern computing technologies enabling students to understand the role of computers in modern society.
Course Outcome:
CO1: Understand the basic concepts and principles of computing and information technology.
CO2: Hands-on Experience with Software Tools.
CO3: Comprehension of Computer Hardware and Software.
CO4: Knowledge of Computer Memory.
CO5: Comprehend the fundamentals of computer networks and internet technologies.
CO6: Aware of current and emerging trends in information technology.

This practical course provides hands-on training in computer assembly/disassembly, operating system and software installation, drivers, I/O devices, security, troubleshooting, and dual-OS configuration. It also covers MS Word, Excel, PowerPoint, Internet applications, cloud productivity, online collaboration, forms, and AI tools for developing essential digital and professional skills.
Course Outcome:
CO1: Assemble and disassemble a computer system, correctly identifying internal components and following ESD/safety precautions.
CO2: Install and configure Microsoft client operating systems and set up basic and advanced I/O devices/peripherals (printers, scanners, webcams, storage, projectors, etc.).
CO3: Install and configure hardware drivers, and set up and troubleshoot dual/multiple operating system environments.
CO4: Install and use utility software for system management, optimization, and maintenance.
CO5: Configure system security tools such as firewall and antivirus software to protect a computer system.
CO6: Create, format, and manage professional documents, spreadsheets, and presentations using MS Word, Excel, and PowerPoint.

This course develops problem-solving and analytical skills through topics such as matrix operations, inverse and rank of matrices, eigenvalues and eigenvectors, counting techniques, series, logical connectives, equivalence, tautologies, and contradictions.
Course Outcome:
CO1. Understand advanced concepts in pure and applied mathematics.
CO2: Apply mathematical theories to interdisciplinary problems.
CO3: Analyze complex mathematical structures and develop rigorous proofs.
CO4: Use numerical and computational techniques for solving scientific problems.
CO5: Conduct mathematical research and communicate findings effectively.

This course provides a comprehensive understanding of number systems, digital codes, logic gates, Boolean algebra, and Boolean expression simplification using Karnaugh maps, combinational and sequential circuits, adders, subtractors, multiplexers, demultiplexers, encoders, decoders, flip-flops, and synchronous/asynchronous counters, developing skills in digital logic design.
Course Outcome:
CO1: Convert numbers between different number systems and perform binary arithmetic using complement methods.
CO2: Apply basic and universal logic gates to implement digital logic functions.
CO3: Simplify Boolean expressions using Boolean laws, De Morgan's theorems, and K-Map minimization techniques.
CO4: Design combinational logic circuits like adders, subtractors, multiplexers, demultiplexers, encoders, and decoders.
CO5: Design and analyse sequential circuits using latches and various flip-flops.
CO6: Design and analyse synchronous and asynchronous digital counters for practical applications.

This course develops effective communication and English language skills and focuses on pronunciation, listening skills, audio-visual aids, body language, presentation techniques, audience analysis, and effective oral presentations for professional communication.
Course Outcome:
CO1: Use standard English aptly in listening, speaking, and communicative situations.
CO2: Write error-free sentences and short texts in English Language.
CO3: Comprehend reading passages effectively.
CO4: Demonstrate use of apt and relevant vocabulary at lower intermediate to intermediate level.
CO5: Develop effective oral presentation skills, including audience analysis and body language.
CO6: Apply audio-visual aids to enhance communication and improve pronunciation.

This course introduces Python programming with an emphasis on data science applications, covering Python fundamentals, data structures, functions, and an introduction to data handling using libraries such as NumPy and Pandas, enabling students to write Python programs for basic data analysis tasks.
Course Outcome:
CO1: Understand Python programming fundamentals, including data types, operators, and control structures.
CO2: Apply functions, modules, and object-oriented concepts to write structured Python programs.
CO3: Use Python data structures such as lists, tuples, and dictionaries for data handling.
CO4: Perform basic data manipulation using the NumPy library.
CO5: Perform data analysis tasks using the Pandas library.
CO6: Apply Python programming concepts to solve introductory data science problems.

This practical course provides hands-on experience in Python programming for data science, covering basic Python programs, data structures, and practical exercises using NumPy and Pandas for data handling and analysis, enabling students to apply Python skills to simple data science tasks.
Course Outcome:
CO1: Write and execute basic Python programs using variables, operators, and control structures.
CO2: Implement functions and use Python data structures for data organization.
CO3: Perform array-based operations using the NumPy library.
CO4: Load, clean, and manipulate datasets using the Pandas library.
CO5: Perform basic exploratory data analysis using Python libraries.
CO6: Apply Python programming skills to complete a simple data science exercise.

This course develops an entrepreneurial mindset, creativity, innovation, opportunity recognition, and problem-solving skills for identifying and developing new business ideas.
Course Outcome:
CO1: Explain the concept, characteristics, and importance of an entrepreneurial mindset.
CO2: Demonstrate creativity and innovation skills to generate and develop new business ideas.
CO3: Identify business opportunities and apply problem-solving techniques to address entrepreneurial challenges.
CO4: Develop basic business models and evaluate the feasibility of entrepreneurial ventures.
CO5: Demonstrate risk-taking, decision-making, leadership, and teamwork skills in entrepreneurial situations.
CO6: Apply fundamental strategies for planning, launching, and managing a new entrepreneurial venture.

This course focuses on ethical living, emotional growth, moral clarity, inner transformation, and social responsibility through reflective discussions, case studies, stories, debates, and self-analysis activities.
Course Outcome:
CO1: Define and identify core universal human values and relate them to their own lives.
CO2: Demonstrate self-awareness and initiate personal transformation (Human Revolution
CO3: Appreciate the importance of Sewa (selfless service), compassion, and empathy.
CO4: Apply principles of truth, non-violence, and moral responsibility in real-life dilemmas.
CO5: Evaluate ethical decisions involving sacrifice and righteousness through case studies.
CO6: Reflect on the relevance of renunciation and simplicity in modern life for inner peace.

2ND SEMESTER SUBJECTS

This course provides a comprehensive understanding of Object-Oriented Programming using C++, covering OOP concepts, classes and objects, constructors and destructors, dynamic memory allocation, and different types of inheritance, polymorphism, function/operator overloading, virtual and pure virtual functions, exception handling, and file handling to develop robust and reusable C++ applications.
Course Outcome:
CO1: Understand and differentiate between Procedure-Oriented Programming and Object-Oriented Programming concepts, and comprehend the structure and execution of C++ programs.
CO2: Develop C++ programs using classes and objects, incorporating member functions, access specifiers, and object manipulation techniques.
CO3: Implement object lifecycle management using constructors, destructors, and dynamic memory allocation for efficient memory usage.
CO4: Apply various inheritance techniques to create class hierarchies and promote code reuse, including handling access modes and multiple inheritance scenarios.
CO5: Utilize polymorphism through function and operator overloading, virtual and pure virtual functions, and abstract classes for flexible code behavior.
CO6: Handle exceptions and manage file operations using standard C++ constructs to build robust and persistent applications.

This practical course provides hands-on experience in C++ programming and OOP concepts, covering basic programs, classes and objects, arrays, pointers, functions, strings, constructors, destructors, dynamic memory allocation, inheritance, polymorphism, operator overloading, virtual and pure virtual functions, and exception handling through practical problem-solving.
Course Outcome:
CO1: Apply the principles of inheritance and object-oriented design to create structured and efficient code using C++.
CO2: Implement control structures, constructors, and destructors to solve basic computational problems using C++.
CO3: Demonstrate the use of arrays, pointers, and polymorphism for effective memory management and behavior abstraction.
CO4: Use functions, strings, and exception handling to build modular and error-resilient programs.
CO5: Develop programs that involve file handling, static and friend functions, and class-based designs for real-world applications.

This course provides a comprehensive understanding of Operating System concepts, including OS types, services, system calls, kernel and shell, process management, threads, IPC, process scheduling, synchronization, deadlocks, memory management, paging, segmentation, virtual memory, page replacement, disk scheduling, and storage management, developing an understanding of efficient resource utilization.
Course Outcome:
CO1: Understand the definition, types, services, and structure of operating systems.
CO2: Apply knowledge of process management, including process concepts, scheduling, threads, and inter-process communication.
CO3: Analyze process synchronization issues, critical-section problems, and apply synchronization techniques like semaphores.
CO4: Evaluate deadlock concepts, including deadlock characterization, prevention, avoidance, detection, and recovery methods.
CO5: Understand memory management techniques, including logical vs. physical addresses, swapping, contiguous allocation, paging, and segmentation.
CO6: Analyze virtual memory and secondary storage structures, including demand paging, page replacement algorithms, and disk scheduling.

This practical course provides hands-on experience in virtualization, virtual machine creation and management, Linux installation and configuration, security, user/group management, essential Linux commands, file handling, permissions, filters, Vi editor, and Linux shell scripting, developing practical skills for Linux-based system administration.
Course Outcome:
CO1: Master fundamental Linux operating system concepts and commands.
CO2: Develop practical skills in system administration and configuration tasks.
CO3: Gain proficiency in troubleshooting and resolving Linux-based issues.
CO4: Understand security practices specific to Linux environments.
CO5: Apply Linux skills to real-world scenarios and projects effectively.

This course provides a comprehensive understanding of computer organization and architecture, covering number systems, data representation, functional units, Von Neumann architecture, buses, CPU structure, instruction cycles, addressing modes, microoperations, I/O organization, memory hierarchy, cache, DMA, pipelining, parallel processing, Flynn’s taxonomy, and pipeline hazards, developing an understanding of efficient computer system design.
Course Outcome:
CO1: Apply number systems, binary arithmetic, complements, and digital codes to represent and manipulate data in computer systems.
CO2: Explain the organization and functioning of computer systems, including functional units, bus structures, data representation, and the Von Neumann architecture.
CO3: Analyze CPU organization, instruction formats, instruction cycles, addressing modes, interrupts and microoperations for efficient program execution.
CO4: Explain the principles of input/output organization, interface techniques, memory-mapped and isolated I/O, and Direct Memory Access (DMA) operations.
CO5: Analyze memory hierarchy, cache memory, associative memory, address mapping, and memory organization for efficient storage and data access.
CO6: Explain pipelining concepts, pipeline hazards, instruction-level parallelism, Flynn's taxonomy, and parallel processing architectures to evaluate processor performance.

This course introduces the fundamentals of statistics and probability, covering the collection, classification, tabulation, and graphical presentation of data. It further explores measures of central tendency and dispersion, including mean, median, mode, range, mean deviation, standard deviation, and coefficient of variation, developing students’ data analysis and interpretation skills.
Course Outcome:
CO1: Learn to predict the relationship between variables according to their strength, direction, taste of customers.
CO2: Acquire knowledge of Statistics and its limitations and importance in various areas.
CO3: Explicitly outline logical flow of information from broad to most fine-grained and will present all statistical results in logical form based on evidence.
CO4: Use different hypothesis testing methods in verifying the claims.
CO5: Familiar with data reflecting quality characteristics including concepts of independence and association between different types of data.

This course develops English language proficiency and effective communication skills, covering tenses, grammar, subject-verb agreement, vocabulary, discourse management, and functional/spoken English. It further focuses on report writing, formal and informal letters, emails, cover letters, and conversational skills for academic and professional communication.
Course Outcome:
CO1: Apply effective grammatical structures in English Language for speaking and writing.
CO2: Utilize effective note-making techniques in English Language.
CO3: Effectively summarize and paraphrase texts in English Language.
CO4: Demonstrate use of apt and relevant vocabulary at intermediate to upper-intermediate Level.
CO5: Employ cohesive and coherent discourse in English communication.
CO6: Produce well-organized written and spoken texts in English Language.

This course develops professional, career, and interpersonal skills, covering self-introduction, résumé preparation, interview skills, group discussions, career opportunities, and effective presentations. It further focuses on teamwork, active listening, social and cultural etiquette, time management, adaptability, and professional communication for workplace success.
Course Outcome:
CO1: Prepare their résumé on an appropriate template without any grammatical and other errors, using proper syntax.
CO2: Participate in a simulated interview.
CO3: Actively participate in group discussions towards gainful employment.
CO4: Capture a self-interview simulation video regarding the concerned job or role.
CO5: Enlist the common errors generally made by candidates in an interview.
CO6: Participate in Presentation Skills.

This course focuses on developing advanced entrepreneurial skills, innovation, opportunity identification, design thinking, business planning, and strategic decision-making for creating sustainable ventures and scaling entrepreneurial ideas.
Course Outcome:
CO1: Apply advanced entrepreneurial concepts and skills to identify and evaluate business opportunities.
CO2: Use design thinking and innovative approaches to develop creative solutions for real-world problems.
CO3: Analyze market opportunities and develop feasible business models for sustainable ventures.
CO4: Prepare comprehensive business plans by integrating financial, marketing, operational, and strategic considerations.
CO5: Apply strategic decision-making and risk management techniques in entrepreneurial ventures.
CO6: Develop strategies for creating, sustaining, and scaling innovative entrepreneurial ideas and ventures.

This course introduces the fundamental concepts of Artificial Intelligence, covering the history and applications of AI, problem-solving through search techniques, knowledge representation, and an overview of machine learning, enabling students to understand the foundations of intelligent systems.
Course Outcome:
CO1: Understand the fundamental concepts, history, and applications of Artificial Intelligence.
CO2: Explain different types of AI agents and their environments.
CO3: Apply search techniques and problem-solving strategies to solve AI-based problems.
CO4: Understand knowledge representation techniques used in AI systems.
CO5: Understand the basic concepts of machine learning and its role in AI.
CO6: Identify real-world applications and ethical considerations of AI.

This practical course provides hands-on experience in implementing basic Artificial Intelligence concepts, covering search algorithms, simple knowledge-based systems, and introductory machine learning exercises, enabling students to apply AI concepts using programming tools.
Course Outcome:
CO1: Implement basic AI search algorithms to solve simple problems.
CO2: Represent knowledge using simple rule-based structures.
CO3: Implement basic AI agent behaviors for simple environments.
CO4: Use programming tools to perform simple machine learning exercises.
CO5: Evaluate the performance of basic AI models on sample datasets.
CO6: Apply AI concepts to develop a simple intelligent application.

3RD SEMESTER SUBJECTS

This course introduces Python programming fundamentals, covering syntax, data types, operators, control structures, functions, string handling, and an introduction to object-oriented programming, enabling students to design, develop, and debug structured Python programs for problem-solving.
Course Outcome:
CO1: Understand the fundamentals of Python programming, its features, and application areas.
CO2: Apply operators, expressions, and control structures to write logical Python programs.
CO3: Develop and use functions, modules, and string operations for structured programming.
CO4: Implement Python data structures such as lists, tuples, sets, and dictionaries to organize and manipulate data.
CO5: Apply object-oriented programming concepts including classes, objects, and inheritance in Python.
CO6: Handle exceptions and perform file operations to build robust Python applications.

This practical course provides hands-on experience in Python programming, covering basic programs, control structures, functions, string manipulation, lists, tuples, dictionaries, and object-oriented concepts, enabling students to design, test, and implement Python programs for real-world problem-solving.
Course Outcome:
CO1: Write and execute basic Python programs using variables, operators, and control structures.
CO2: Develop modular programs using functions and string-handling techniques.
CO3: Implement and manipulate Python data structures such as lists, tuples, sets, and dictionaries.
CO4: Design programs using object-oriented concepts including classes, objects, and inheritance.
CO5: Handle exceptions and perform file handling operations in Python programs.
CO6: Debug and test Python programs to solve practical computational problems.

This course provides a comprehensive understanding of linear and non-linear data structures, including arrays, stacks, queues, linked lists, trees, and graphs, along with searching and sorting techniques, enabling students to select and implement appropriate data structures for efficient problem-solving.
Course Outcome:
CO1: Understand the concept, classification, and applications of data structures.
CO2: Implement arrays and analyze algorithms using time and space complexity.
CO3: Design and apply stacks and queues to solve computational problems.
CO4: Implement linked lists and perform operations such as insertion, deletion, and traversal.
CO5: Construct and traverse tree structures, including binary and binary search trees.
CO6: Apply graph representations and searching/sorting algorithms for efficient data processing.

This practical course provides hands-on experience in implementing linear and non-linear data structures such as arrays, stacks, queues, linked lists, trees, and graphs, along with searching and sorting algorithms, enabling students to develop efficient programs for data organization and manipulation.
Course Outcome:
CO1: Implement array-based operations and analyze their efficiency.
CO2: Develop programs using stacks and queues for problem-solving.
CO3: Implement singly, doubly, and circular linked lists with various operations.
CO4: Construct and traverse binary trees and binary search trees.
CO5: Implement graph representations and traversal algorithms.
CO6: Apply searching and sorting algorithms to organize and retrieve data efficiently.

This course introduces the principles and practices of software engineering, covering software development life cycle models, requirement analysis, software design, coding standards, testing strategies, and project management, enabling students to apply systematic approaches to develop reliable and maintainable software.
Course Outcome:
CO1: Understand the fundamental concepts, characteristics, and process models of software engineering.
CO2: Perform requirement analysis and prepare software requirement specifications.
CO3: Apply software design principles, including architectural and modular design techniques.
CO4: Apply coding standards and software testing strategies to ensure software quality.
CO5: Understand software project management concepts, including estimation, scheduling, and risk management.
CO6: Apply software maintenance and quality assurance practices to real-world software projects.

This course introduces students to emerging Artificial Intelligence tools and technologies, covering generative AI, prompt engineering, AI-based productivity and content-creation tools, and their applications across domains, enabling students to effectively use AI tools to enhance learning, creativity, and problem-solving.
Course Outcome:
CO1: Understand the fundamental concepts of Artificial Intelligence and emerging AI tools.
CO2: Apply prompt engineering techniques to interact effectively with generative AI tools.
CO3: Use AI-based tools for content creation, documentation, and presentation development.
CO4: Apply AI tools for data analysis, research, and productivity enhancement.
CO5: Evaluate the ethical considerations and limitations of using AI tools.
CO6: Integrate AI tools into academic and real-world problem-solving tasks.

This course develops advanced entrepreneurial thinking, focusing on scaling business ventures, financial planning, marketing strategies, and leadership skills, enabling students to plan, launch, and sustain entrepreneurial ventures in dynamic business environments.
Course Outcome:
CO1: Explain advanced concepts of entrepreneurship related to venture scaling and sustainability.
CO2: Apply financial planning and resource management techniques for entrepreneurial ventures.
CO3: Develop marketing and branding strategies for new business ventures.
CO4: Demonstrate leadership and team-building skills required for managing entrepreneurial ventures.
CO5: Evaluate strategies for scaling and sustaining business ventures in competitive markets.
CO6: Apply entrepreneurial planning techniques to launch a viable business venture.

This course introduces applied data science techniques, covering the data science workflow, data collection and preprocessing, exploratory data analysis, statistical modeling, and an introduction to machine learning, enabling students to apply data science methods to solve practical, real-world problems.
Course Outcome:
CO1: Understand the data science workflow and its key stages.
CO2: Apply data collection and preprocessing techniques on real-world datasets.
CO3: Perform exploratory data analysis and statistical summarization of data.
CO4: Apply basic statistical modeling techniques to analyze data.
CO5: Understand the fundamentals of machine learning within a data science context.
CO6: Apply data science techniques to solve a practical problem and interpret results.

This practical course provides hands-on experience in applying data science techniques, covering data collection, cleaning, exploratory analysis, statistical modeling, and basic machine learning tasks using data science tools, enabling students to work through a complete applied data science project.
Course Outcome:
CO1: Collect and preprocess real-world datasets for analysis.
CO2: Perform exploratory data analysis using data science tools.
CO3: Apply statistical modeling techniques to analyze datasets.
CO4: Implement basic machine learning models on sample data.
CO5: Visualize and interpret results of a data science project.
CO6: Complete an end-to-end applied data science project and present findings.

This course introduces the principles and techniques of data visualization, covering visualization types, design principles, and the use of visualization tools and libraries to create charts, graphs, and dashboards, enabling students to effectively communicate insights from data.
Course Outcome:
CO1: Understand the fundamental principles and importance of data visualization.
CO2: Select appropriate chart types for different kinds of data.
CO3: Apply design principles to create clear and effective visualizations.
CO4: Use visualization tools and libraries to create charts and graphs.
CO5: Design interactive dashboards to represent data insights.
CO6: Interpret and communicate insights effectively through visualizations.

This practical course provides hands-on experience in creating data visualizations, covering the use of visualization libraries and tools to design charts, graphs, and dashboards from real-world datasets, enabling students to build effective visual representations of data.
Course Outcome:
CO1: Create basic charts and graphs using data visualization tools.
CO2: Apply appropriate visualization types for different datasets.
CO3: Customize visual elements such as colors, labels, and legends.
CO4: Build interactive dashboards using visualization tools.
CO5: Visualize real-world datasets to identify trends and patterns.
CO6: Present data insights effectively using visualizations.

This course introduces the concepts and techniques of feature engineering, covering feature extraction, feature selection, feature transformation, and handling of missing and categorical data, enabling students to prepare high-quality features for building effective machine learning models.
Course Outcome:
CO1: Understand the importance and role of feature engineering in machine learning.
CO2: Apply techniques for handling missing values and outliers in datasets.
CO3: Perform feature extraction and transformation on raw data.
CO4: Encode categorical variables using appropriate techniques.
CO5: Apply feature selection techniques to identify relevant features.
CO6: Evaluate the impact of engineered features on model performance.

This practical course provides hands-on experience in feature engineering techniques, covering data cleaning, feature extraction, transformation, encoding, and selection using data science tools, enabling students to prepare datasets for effective machine learning model building.
Course Outcome:
CO1: Handle missing values and outliers in real-world datasets.
CO2: Perform feature extraction and transformation on sample data.
CO3: Encode categorical variables using appropriate encoding techniques.
CO4: Apply feature scaling techniques to normalize data.
CO5: Apply feature selection methods to identify important features.
CO6: Prepare a complete feature-engineered dataset for a machine learning task.

4TH SEMESTER SUBJECTS

This course introduces the fundamentals of Artificial Intelligence and Soft Computing, covering problem-solving through search techniques, knowledge representation, expert systems, fuzzy logic, artificial neural networks, and genetic algorithms, enabling students to understand and apply intelligent computing techniques to real-world problems.
Course Outcome:
CO1: Understand the fundamental concepts, history, and applications of Artificial Intelligence.
CO2: Apply search techniques and problem-solving strategies to solve AI-based problems.
CO3: Understand knowledge representation techniques and the working of expert systems.
CO4: Apply fuzzy logic concepts and fuzzy set operations to handle uncertainty in real-world problems.
CO5: Understand the fundamentals of artificial neural networks and their learning mechanisms.
CO6: Apply genetic algorithms and other soft computing techniques to optimize computational problems.

This practical course provides hands-on experience in implementing Artificial Intelligence and Soft Computing techniques, including search algorithms, knowledge-based systems, fuzzy logic operations, neural network models, and genetic algorithms, enabling students to design and evaluate intelligent systems for problem-solving.
Course Outcome:
CO1: Implement basic AI search algorithms to solve computational problems.
CO2: Develop simple knowledge-based and rule-based expert systems.
CO3: Implement fuzzy set operations and fuzzy inference systems.
CO4: Design and simulate artificial neural network models for pattern recognition.
CO5: Implement genetic algorithms to solve optimization problems.
CO6: Evaluate and compare the performance of different soft computing techniques.

This course provides a comprehensive understanding of computer network fundamentals, covering network models, the OSI and TCP/IP reference models, data communication, network devices, addressing, routing, and network security, enabling students to understand the design and functioning of modern computer networks.
Course Outcome:
CO1: Understand the basic concepts, types, and topologies of computer networks.
CO2: Explain the layered architecture of the OSI and TCP/IP reference models.
CO3: Apply data link layer concepts, including error detection, correction, and medium access control.
CO4: Understand network layer concepts, including IP addressing, subnetting, and routing algorithms.
CO5: Apply transport layer protocols and concepts for reliable data communication.
CO6: Understand application layer protocols and fundamentals of network security.

This practical course provides hands-on experience in computer networking concepts, including network cabling, IP addressing, subnetting, configuration of network devices, and use of networking commands and simulation tools, enabling students to design, configure, and troubleshoot basic computer networks.
Course Outcome:
CO1: Identify and work with networking devices, cables, and connectors.
CO2: Configure IP addressing and subnetting for a given network topology.
CO3: Use networking commands and utilities for network diagnosis and troubleshooting.
CO4: Configure basic routing and switching using networking simulation tools.
CO5: Implement and test simple client-server network applications.
CO6: Analyze network traffic and apply basic network security configurations.

This course introduces the fundamental concepts of database management systems, covering data models, relational database design, normalization, SQL, transaction management, and concurrency control, enabling students to design, implement, and manage efficient and reliable database systems.
Course Outcome:
CO1: Understand the fundamental concepts, architecture, and advantages of database management systems.
CO2: Design entity-relationship models and convert them into relational database schemas.
CO3: Apply normalization techniques to eliminate redundancy and ensure data integrity.
CO4: Write and execute SQL queries for data definition, manipulation, and retrieval.
CO5: Understand transaction management concepts, including ACID properties and concurrency control.
CO6: Understand database recovery techniques and basics of database security.

This practical course provides hands-on experience in designing and implementing relational databases, covering ER modeling, table creation, SQL queries, joins, subqueries, views, and transaction control, enabling students to develop and manage functional database applications.
Course Outcome:
CO1: Design ER diagrams and convert them into relational database schemas.
CO2: Create and manage database tables using SQL data definition commands.
CO3: Perform data manipulation operations using SQL insert, update, and delete commands.
CO4: Write complex SQL queries involving joins, subqueries, and aggregate functions.
CO5: Implement views, indexes, and constraints to ensure data integrity.
CO6: Apply transaction control commands to manage database transactions effectively.

This course prepares students for the role of an AI analyst, covering AI-driven data analysis, model evaluation, business application of AI insights, and effective communication of AI-based findings, enabling students to analyze and interpret AI system outputs for informed decision-making.
Course Outcome:
CO1: Understand the role and responsibilities of an AI analyst.
CO2: Apply AI-driven techniques to analyze structured and unstructured data.
CO3: Evaluate the performance and outputs of AI models.
CO4: Interpret AI-generated insights to support business decision-making.
CO5: Communicate AI-based findings effectively to stakeholders.
CO6: Apply AI analysis techniques to solve real-world business problems.

This practical course provides hands-on experience in performing AI-based data analysis, covering the use of AI tools for data exploration, model evaluation, and generating actionable insights, enabling students to apply AI analyst skills to practical business scenarios.
Course Outcome:
CO1: Use AI tools to explore and analyze sample datasets.
CO2: Evaluate outputs of pre-built AI models on given data.
CO3: Generate insights and reports from AI model outputs.
CO4: Apply AI-based analysis techniques to a business case study.
CO5: Present AI-driven findings using appropriate visualization tools.
CO6: Develop a small project applying AI analyst skills to a real scenario.

This course introduces the concepts and techniques of big data analytics, covering big data characteristics, distributed processing frameworks, analytics techniques, and real-world big data applications, enabling students to analyze large-scale datasets to extract meaningful insights.
Course Outcome:
CO1: Understand the characteristics, challenges, and applications of big data.
CO2: Explain distributed storage and processing frameworks for big data.
CO3: Apply big data analytics techniques to analyze large datasets.
CO4: Understand batch and real-time data processing concepts.
CO5: Apply visualization techniques to represent big data analytics results.
CO6: Analyze real-world case studies involving big data analytics.

This practical course provides hands-on experience in big data analytics tools and frameworks, covering data ingestion, distributed processing, and analytics on large datasets, enabling students to gain practical exposure to analyzing big data.
Course Outcome:
CO1: Set up and configure basic big data analytics tools.
CO2: Import and process large datasets using big data frameworks.
CO3: Perform distributed data processing tasks on sample big data.
CO4: Apply analytics techniques to extract insights from large datasets.
CO5: Visualize results obtained from big data analytics.
CO6: Demonstrate a simple end-to-end big data analytics workflow.

This course introduces the concepts of data mining and data warehousing, covering data warehouse architecture, OLAP operations, data preprocessing, and mining techniques such as classification, clustering, and association rule mining, enabling students to extract meaningful patterns from large datasets.
Course Outcome:
CO1: Understand the concepts, architecture, and components of data warehousing.
CO2: Apply OLAP operations for multidimensional data analysis.
CO3: Perform data preprocessing techniques on real-world datasets.
CO4: Apply classification techniques to build predictive data mining models.
CO5: Apply clustering techniques to group and analyze data patterns.
CO6: Apply association rule mining to discover relationships within datasets.

This practical course provides hands-on experience in data warehousing and data mining techniques, covering data preprocessing, OLAP operations, and implementation of classification, clustering, and association rule mining algorithms, enabling students to analyze datasets using data mining tools.
Course Outcome:
CO1: Perform data preprocessing tasks such as cleaning and transformation.
CO2: Implement OLAP operations for multidimensional data analysis.
CO3: Implement classification algorithms using data mining tools.
CO4: Implement clustering algorithms to group similar data patterns.
CO5: Implement association rule mining algorithms to discover data relationships.
CO6: Interpret and present results obtained from data mining experiments.

This course develops logical reasoning and analytical problem-solving skills, covering numerical ability, logical reasoning, data interpretation, and quantitative aptitude, enabling students to enhance their analytical thinking for academic and competitive examinations.
Course Outcome:
CO1: Apply numerical ability concepts to solve quantitative problems.
CO2: Solve logical reasoning problems using systematic approaches.
CO3: Interpret and analyze data presented in various formats.
CO4: Apply problem-solving techniques to competitive examination-style questions.
CO5: Improve speed and accuracy in solving analytical problems.

This course introduces the fundamental concepts of environmental science, covering natural resources, ecosystems, biodiversity, environmental pollution, and sustainable development, enabling students to understand environmental issues and develop responsible practices towards environmental conservation.
Course Outcome:
CO1: Understand the basic concepts and importance of environmental science.
CO2: Explain the structure and function of ecosystems and biodiversity conservation.
CO3: Identify causes, effects, and control measures of environmental pollution.
CO4: Understand the concept and importance of sustainable development.
CO5: Apply environmentally responsible practices in personal and professional life.

This course focuses on advanced entrepreneurial practices, covering business scaling strategies, innovation management, funding and investment options, and sustainable business practices, enabling students to develop and manage growth-oriented entrepreneurial ventures.
Course Outcome:
CO1: Explain advanced strategies for scaling and growing a business venture.
CO2: Apply innovation management techniques to entrepreneurial ventures.
CO3: Understand various funding and investment options available for startups.
CO4: Develop strategies for building sustainable and socially responsible businesses.
CO5: Evaluate risks and challenges associated with entrepreneurial growth.
CO6: Apply entrepreneurial concepts to develop a business growth plan.

5TH SEMESTER SUBJECTS

This course introduces the fundamentals of Cloud Computing, covering cloud service and deployment models, virtualization, cloud architecture, cloud storage, and major cloud platforms, enabling students to understand how cloud technologies are designed, deployed, and utilized for scalable computing solutions.
Course Outcome:
CO1: Understand the fundamental concepts, characteristics, and evolution of Cloud Computing.
CO2: Differentiate between cloud service models (IaaS, PaaS, SaaS) and deployment models.
CO3: Understand virtualization concepts and their role in enabling cloud infrastructure.
CO4: Explain cloud architecture, storage, and networking components.
CO5: Compare and evaluate major cloud service platforms and their offerings.
CO6: Identify the benefits, challenges, and real-world applications of cloud computing.

This practical course provides hands-on experience with cloud platforms, covering account and resource setup, virtual machine creation, cloud storage configuration, and deployment of basic applications, enabling students to gain practical exposure to working with cloud computing environments.
Course Outcome:
CO1: Create and configure accounts and resources on a cloud computing platform.
CO2: Set up and manage virtual machines on a cloud environment.
CO3: Configure and use cloud storage services for data management.
CO4: Deploy basic applications and services using cloud platform tools.
CO5: Monitor and manage cloud resources for optimal utilization.
CO6: Apply basic access control and security settings within a cloud environment.

This course provides a comprehensive understanding of network security principles and cryptographic techniques, covering symmetric and asymmetric encryption, hashing, digital signatures, authentication protocols, and network security mechanisms, enabling students to design and implement secure communication systems.
Course Outcome:
CO1: Understand the fundamental concepts and goals of network security.
CO2: Apply symmetric key cryptographic algorithms for secure data transmission.
CO3: Apply asymmetric key cryptographic algorithms and public key infrastructure concepts.
CO4: Use hashing techniques and digital signatures to ensure data integrity and authentication.
CO5: Understand network security protocols and mechanisms for securing communication.
CO6: Analyze common network attacks and apply appropriate countermeasures.

This practical course provides hands-on experience in implementing cryptographic algorithms and network security techniques, including encryption/decryption, hashing, digital signatures, and basic security tools, enabling students to apply security concepts to protect data and communication.
Course Outcome:
CO1: Implement classical and modern symmetric encryption algorithms.
CO2: Implement asymmetric encryption algorithms for secure key exchange.
CO3: Apply hashing algorithms to verify data integrity.
CO4: Generate and verify digital signatures for authentication purposes.
CO5: Use security tools to analyze and monitor network traffic.
CO6: Apply basic security configurations to protect systems from common attacks.

This course develops technical writing and documentation skills, covering the principles of clear and precise writing, technical reports, manuals, research papers, and professional documentation, enabling students to effectively communicate technical information for academic and workplace purposes.
Course Outcome:
CO1: Understand the principles and characteristics of effective technical writing.
CO2: Write clear, concise, and well-structured technical documents and reports.
CO3: Apply appropriate formatting, style, and referencing standards in technical writing.
CO4: Prepare user manuals, proposals, and process documentation for technical audiences.
CO5: Develop research papers and technical articles following academic writing conventions.
CO6: Edit and proofread technical documents to improve clarity and accuracy.

This course evaluates the practical industry exposure gained by students during their summer training, covering the assessment of technical skills acquired, project work undertaken, and professional experience gained, enabling students to consolidate and present their learning from real-world work environments.
Course Outcome:
CO1: Demonstrate technical skills and knowledge acquired during industrial summer training.
CO2: Document the training experience through a structured training report.
CO3: Present the work undertaken during training in a clear and organized manner.
CO4: Reflect on the practical application of academic concepts in a professional setting.
CO5: Evaluate personal and professional growth achieved through industry exposure.

This course enables students to apply the knowledge and skills gained throughout the programme to design and develop a substantial project, covering problem identification, requirement analysis, system design, implementation, and documentation, fostering independent and applied learning through a real-world capstone project.
Course Outcome:
CO1: Identify a real-world problem and define project objectives and scope.
CO2: Perform requirement analysis and design an appropriate system or solution.
CO3: Apply technical skills and tools to implement the proposed project.
CO4: Test and evaluate the developed project against defined requirements.
CO5: Prepare comprehensive project documentation following standard formats.
CO6: Present and defend the project work through demonstrations and reports.

This course introduces the fundamentals of deep learning and neural networks, covering the structure of artificial neural networks, activation functions, backpropagation, convolutional and recurrent neural networks, enabling students to design and train deep learning models for various applications.
Course Outcome:
CO1: Understand the fundamental concepts and architecture of artificial neural networks.
CO2: Explain the working of activation functions and the backpropagation algorithm.
CO3: Understand the architecture and applications of convolutional neural networks.
CO4: Understand the architecture and applications of recurrent neural networks.
CO5: Apply techniques to train and optimize deep learning models.
CO6: Evaluate the performance of deep learning models on sample tasks.

This practical course provides hands-on experience in building and training neural networks, covering implementation of basic neural networks, convolutional neural networks, and recurrent neural networks using deep learning frameworks, enabling students to develop deep learning models for practical tasks.
Course Outcome:
CO1: Implement a basic artificial neural network using a deep learning framework.
CO2: Train and evaluate neural network models on sample datasets.
CO3: Implement convolutional neural networks for image-based tasks.
CO4: Implement recurrent neural networks for sequential data tasks.
CO5: Apply techniques to improve model performance and reduce overfitting.
CO6: Develop and demonstrate a simple deep learning application.

This course introduces the fundamentals of Generative Artificial Intelligence, covering generative models, large language models, prompt-based generation, and applications of generative AI in text, image, and content creation, enabling students to understand and apply generative AI techniques and tools.
Course Outcome:
CO1: Understand the fundamental concepts and evolution of generative AI.
CO2: Explain the working principles of generative models.
CO3: Understand the basics of large language models and their capabilities.
CO4: Apply generative AI tools for text and content generation.
CO5: Explore generative AI applications in image and multimedia creation.
CO6: Evaluate the ethical considerations and limitations of generative AI.

This practical course provides hands-on experience with generative AI tools and models, covering text generation, image generation, and content creation using generative AI platforms, enabling students to apply generative AI techniques to practical use cases.
Course Outcome:
CO1: Use generative AI tools to generate text-based content.
CO2: Apply prompt design techniques to improve generative AI outputs.
CO3: Use generative AI tools to create images and visual content.
CO4: Apply generative AI tools for content creation and productivity tasks.
CO5: Evaluate the quality and accuracy of generative AI outputs.
CO6: Develop a small project using generative AI tools.

This course introduces the fundamentals of Natural Language Processing, covering text preprocessing, language modeling, part-of-speech tagging, sentiment analysis, and an introduction to NLP applications, enabling students to understand and apply techniques for processing and analyzing human language data.
Course Outcome:
CO1: Understand the fundamental concepts and applications of Natural Language Processing.
CO2: Apply text preprocessing techniques such as tokenization and stemming.
CO3: Understand basic language modeling techniques used in NLP.
CO4: Apply part-of-speech tagging and named entity recognition techniques.
CO5: Perform sentiment analysis on textual data.
CO6: Explore real-world applications of NLP techniques.

This practical course provides hands-on experience in Natural Language Processing techniques, covering text preprocessing, feature extraction, and implementation of basic NLP tasks such as sentiment analysis using NLP libraries and tools, enabling students to build simple NLP applications.
Course Outcome:
CO1: Perform text preprocessing tasks such as tokenization and stopword removal.
CO2: Extract features from text data using NLP techniques.
CO3: Implement part-of-speech tagging on sample text data.
CO4: Perform sentiment analysis using NLP libraries.
CO5: Build a simple text classification model using NLP techniques.
CO6: Develop and demonstrate a basic NLP application.

This course focuses on advanced entrepreneurial execution, covering business plan development, pitching techniques, risk management, and strategies for sustaining and exiting a venture, enabling students to refine and present a comprehensive entrepreneurial plan.
Course Outcome:
CO1: Develop a comprehensive business plan for an entrepreneurial venture.
CO2: Apply effective pitching techniques to present a business idea.
CO3: Identify and manage risks associated with running a business venture.
CO4: Understand strategies for sustaining long-term business growth.
CO5: Explore exit strategies and succession planning for business ventures.
CO6: Present a complete entrepreneurial plan for evaluation.

6TH SEMESTER (OPTION: A) SUBJECTS

This course provides students with structured industry exposure through a period of training in an organizational environment, covering real-world work practices, professional conduct, and application of academic knowledge to practical tasks, enabling students to develop workplace-ready skills and industry insight.
Course Outcome:
CO1: Understand the working environment, culture, and practices of an industrial organization.
CO2: Apply academic knowledge and skills to practical, real-world work assignments.
CO3: Develop professional and workplace communication and interpersonal skills.
CO4: Document the training experience through a structured industrial training report.
CO5: Evaluate personal and professional growth achieved through industrial exposure.

This course is the culmination of the capstone project initiated earlier, focusing on the complete implementation, testing, refinement, and final deployment of the project, along with comprehensive documentation and presentation, enabling students to demonstrate end-to-end application of their academic learning to a real-world solution.
Course Outcome:
CO1: Refine the project design based on feedback from the earlier capstone phase.
CO2: Complete the implementation of the proposed system or solution.
CO3: Perform thorough testing and validation of the developed project.
CO4: Optimize and finalize the project for deployment or practical use.
CO5: Prepare complete project documentation, including reports and user guides.
CO6: Present and defend the completed project before an evaluation panel.

6TH SEMESTER (OPTION: B) SUBJECTS

This course provides a comprehensive understanding of information security principles and cyber law, covering security threats, risk management, security policies, data protection, and the legal and ethical framework governing cyberspace, enabling students to safeguard information systems and understand cyber regulations.
Course Outcome:
CO1: Understand the fundamental concepts and importance of information security.
CO2: Identify information security threats, vulnerabilities, and risk management practices.
CO3: Apply security policies and controls to protect organizational information assets.
CO4: Understand the fundamentals of cyber law and Information Technology Act provisions.
CO5: Analyze cybercrimes, digital evidence, and legal remedies available under cyber law.
CO6: Apply ethical and legal principles while handling information and digital resources.

This course introduces the fundamentals of the Internet of Things, covering IoT architecture, sensors and actuators, communication protocols, IoT platforms, and application domains, enabling students to understand the design and functioning of connected smart devices and systems.
Course Outcome:
CO1: Understand the fundamental concepts, architecture, and applications of IoT.
CO2: Explain the working of sensors, actuators, and IoT hardware components.
CO3: Understand IoT communication protocols and networking technologies.
CO4: Explore IoT platforms and cloud integration for data management.
CO5: Understand data handling and analytics techniques used in IoT systems.
CO6: Identify security challenges and best practices in IoT deployments.

This practical course provides hands-on experience in building IoT applications, covering sensor and actuator interfacing, microcontroller programming, communication protocols, and cloud connectivity, enabling students to design and implement simple IoT-based projects.
Course Outcome:
CO1: Interface sensors and actuators with microcontroller/IoT development boards.
CO2: Write and upload programs to control IoT hardware components.
CO3: Implement communication between IoT devices using standard protocols.
CO4: Connect IoT devices to cloud platforms for data transmission and monitoring.
CO5: Collect and visualize sensor data using IoT dashboards.
CO6: Design and demonstrate a simple end-to-end IoT application.

This course introduces the fundamentals of Big Data, covering the characteristics of big data, distributed storage and processing frameworks, the Hadoop ecosystem, and data analytics techniques, enabling students to understand how large-scale data is stored, processed, and analyzed.
Course Outcome:
CO1: Understand the characteristics, sources, and challenges of Big Data.
CO2: Explain the architecture and components of the Hadoop ecosystem.
CO3: Understand distributed storage concepts using the Hadoop Distributed File System.
CO4: Apply the MapReduce programming model for distributed data processing.
CO5: Explore Big Data processing tools and frameworks for analytics.
CO6: Understand the applications of Big Data across various domains.

This practical course provides hands-on experience in Big Data tools and frameworks, covering Hadoop installation and configuration, HDFS operations, MapReduce programming, and basic data processing tasks, enabling students to gain practical exposure to handling large-scale datasets.
Course Outcome:
CO1: Set up and configure a basic Hadoop environment.
CO2: Perform file and directory operations using HDFS commands.
CO3: Write and execute simple MapReduce programs for data processing.
CO4: Import and process datasets using Big Data tools.
CO5: Perform basic data analysis tasks on large datasets.
CO6: Demonstrate the working of a simple Big Data processing pipeline.

This practical course provides hands-on experience with version control systems, covering repository creation, branching, merging, conflict resolution, and collaborative workflows using tools such as Git and GitHub, enabling students to manage source code effectively in team-based software development.
Course Outcome:
CO1: Understand the concept and importance of version control systems.
CO2: Create and manage repositories using Git.
CO3: Perform branching, merging, and conflict resolution in a Git repository.
CO4: Collaborate on projects using remote repositories and platforms such as GitHub.
CO5: Apply best practices for commit history and version tracking.
CO6: Manage a collaborative software project using a version control workflow.

This course introduces the ethical considerations surrounding Artificial Intelligence, covering bias and fairness, transparency, accountability, privacy concerns, and responsible AI development practices, enabling students to understand and apply ethical principles in the design and deployment of AI systems.
Course Outcome:
CO1: Understand the fundamental concepts and importance of AI ethics.
CO2: Identify sources of bias and fairness issues in AI systems.
CO3: Understand the importance of transparency and explainability in AI.
CO4: Explain accountability and governance considerations in AI systems.
CO5: Understand privacy concerns associated with AI technologies.
CO6: Apply responsible AI principles in the design and use of AI systems.

This practical course provides hands-on experience in evaluating ethical issues in AI systems, covering bias detection, fairness assessment, and case-based analysis of ethical dilemmas using sample AI models and datasets, enabling students to apply AI ethics principles practically.
Course Outcome:
CO1: Analyze sample datasets to identify potential sources of bias.
CO2: Evaluate AI model outputs for fairness issues.
CO3: Apply basic bias mitigation techniques to sample models.
CO4: Analyze case studies involving ethical dilemmas in AI applications.
CO5: Assess AI systems against responsible AI guidelines.
CO6: Prepare a report evaluating the ethical implications of an AI system.

This course introduces the principles and techniques of prompt engineering for generative AI models, covering prompt design strategies, prompt optimization, context management, and best practices for interacting effectively with large language models, enabling students to craft effective prompts for various AI applications.
Course Outcome:
CO1: Understand the fundamental concepts and importance of prompt engineering.
CO2: Apply prompt design strategies to generate desired AI outputs.
CO3: Understand techniques for optimizing and refining prompts.
CO4: Apply context management techniques in multi-turn AI interactions.
CO5: Design prompts for specific tasks such as summarization and content generation.
CO6: Evaluate the effectiveness of prompts and iterate for improved results.

This practical course provides hands-on experience in designing and testing prompts for generative AI models, covering prompt creation, optimization, and evaluation across various tasks using AI tools and platforms, enabling students to apply prompt engineering techniques practically.
Course Outcome:
CO1: Design basic prompts for generative AI tools to perform specific tasks.
CO2: Apply prompt refinement techniques to improve output quality.
CO3: Test prompts across different generative AI models and platforms.
CO4: Design prompts for tasks such as summarization, translation, and content creation.
CO5: Evaluate and compare outputs generated from different prompt designs.
CO6: Develop a small project demonstrating effective prompt engineering.

This course focuses on the complete execution of a capstone project, covering final implementation, integration, testing, deployment, and comprehensive documentation, enabling students to demonstrate the end-to-end application of their academic learning through a fully realized project.
Course Outcome:
CO1: Finalize the design and scope of the capstone project.
CO2: Complete the implementation and integration of all project components.
CO3: Conduct comprehensive testing and validation of the project.
CO4: Deploy the completed project in a real or simulated environment.
CO5: Prepare complete project documentation, including reports and manuals.
CO6: Present and defend the completed project before an evaluation panel.

fees

Details

Amount

Programme Fees (per Semester)

₹70000

Examination Fees

₹3000

International Fees (per Year)

$3700

Fee Slab

Slab >=60% - 74.99% >=75% - 89.99% >=90% & Above
Fee ₹65000 ₹60000 ₹55000

Students can avail these slots depending on the marks they have scored. Each slot reflects a different academic range, helping students understand where they stand and what benefits they qualify for.

Programme Outcomes

Understanding of robotic process automation, how it works, and the different factors and parameters that influence it. Process Management (Design, Management, and Automation), Basic Electronics, Sensor Technologies, IoT, and Robotic Automation are all needed.
Master the concepts and principles of Machine Learning, Artificial Intelligence, and the Internet of Things. Learn about significant uses of Artificial Intelligence across different use cases in different industry verticals Demonstrate information to survey cultural, wellbeing, security, lawful and social issues and the resulting obligations applicable to proficient practice. Understand the effect of the computational arrangements in cultural and ecological settings, and show the information and need for reasonable turn of events.

Programme Specific Outcomes

The alumni will actually want to adjust to the quick changing universe of Information Technology needs. The alumni will become powerful colleagues and through inventive philosophies, they will actually want to address the social, specialized and business challenges. The alumni will actually want to impart proficiently and successfully. The alumni will actually want to work in numerous disciplinary groups Function successfully as an individual, and as a part or pioneer in assorted groups, and in multidisciplinary settings.

Salient Features

Apply moral standards and focus on proficient morals and obligations and standards of the expert practice. Function successfully as an individual, and as a part or pioneer in assorted/multidisciplinary groups. A capacity to impart adequately.

Infrastructure

Laboratory

Laboratory

What better way to learn working of anything than to get exposed to it first hand!. The specialized laboratories are equipped to conduct advanced practical and research work.