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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. It further covers pointers, structures, unions, storage classes, and file handling, enabling students to develop structured and efficient programs and strengthen their problem-solving skills.

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. It also introduces file handling, enabling students to develop, test, and implement C programs for solving real-world computational problems.

This course provides a comprehensive understanding of computer fundamentals, hardware, software, memory, operating systems, and input/output devices, along with modern computing technologies. It also covers computer networks, Internet, WWW, web browsers, cloud technology, e-commerce, mobile technologies, cybersecurity, IoT, and emerging IT trends, enabling students to understand the role of computers in modern society.

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.

This course covers matrices, determinants, linear algebra, permutations and combinations, arithmetic and geometric progressions, and logical reasoning. It 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.

This course provides a comprehensive understanding of number systems, digital codes, logic gates, Boolean algebra, and Boolean expression simplification using Karnaugh maps. It further covers combinational and sequential circuits, adders, subtractors, multiplexers, demultiplexers, encoders, decoders, flip-flops, and synchronous/asynchronous counters, developing skills in digital logic design.

This course develops effective communication and English language skills, covering the fundamentals of communication, barriers, spoken and written communication, grammar, vocabulary, sentence structure, paragraph writing, and functional English. It also focuses on pronunciation, listening skills, audio-visual aids, body language, presentation techniques, audience analysis, and effective oral presentations for professional communication.

This course introduces Python programming fundamentals, covering variables, data types, operators, control structures, functions, collections, modules, and object-oriented programming concepts. Students develop structured and readable programs using exception handling, file handling, debugging, code organization, and Python libraries, with emphasis on problem-solving and programming practices. The course emphasizes clean coding principles, meaningful naming, modularity, code readability, documentation, refactoring, testing, and coding standards to develop maintainable and reliable software.

This lab provides hands-on practice in Python programming and problem-solving through programs involving data types, control structures, functions, lists, tuples, dictionaries, strings, and object-oriented programming.Students implement practical applications using modules, file handling, exception handling, debugging, and basic Python libraries, while applying structured programming techniques.The lab develops skills in clean code practices, modular design, meaningful naming, documentation, refactoring, testing, and writing readable and maintainable Python programs.

This course develops an entrepreneurial mindset, creativity, innovation, opportunity recognition, and problem-solving skills for identifying and developing new business ideas. It also covers business model development, risk-taking, leadership, teamwork, decision-making, and basic strategies for launching and managing entrepreneurial ventures.

This course focuses on self-awareness, personal development, love, compassion, truth, non-violence, empathy, righteousness, sacrifice, and renunciation as essential human values. It promotes ethical living, emotional growth, moral clarity, inner transformation, and social responsibility through reflective discussions, case studies, stories, debates, and self-analysis activities.

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. It further explores polymorphism, function/operator overloading, virtual and pure virtual functions, exception handling, and file handling to develop robust and reusable C++ 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, and dynamic memory allocation. It further develops skills in inheritance, polymorphism, operator overloading, virtual and pure virtual functions, and exception handling through practical problem-solving.

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, and synchronization. It further covers deadlocks, memory management, paging, segmentation, virtual memory, page replacement, disk scheduling, and storage management, developing an understanding of efficient resource utilization.

This practical course provides hands-on experience in virtualization, virtual machine creation and management, Linux installation and configuration, security, user/group management, and essential Linux commands. It further covers directory and file handling, permissions, filters, Vi editor, and Linux shell scripting, developing practical skills for Linux-based system administration.

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, and microoperations. It further explores I/O organization, memory hierarchy, cache, DMA, pipelining, parallel processing, Flynn’s taxonomy, and pipeline hazards, developing an understanding of efficient computer system design.

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.

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 note-making, summarizing, paraphrasing, essay and report writing, para jumbles, formal and informal letters, emails, cover letters, and conversational skills for academic and professional communication.

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, cognitive and non-cognitive skills, active listening, social and cultural etiquette, time management, adaptability, and professional communication for workplace success.

This course focuses on developing advanced entrepreneurial skills, innovation, opportunity identification, design thinking, business planning, and strategic decision-making for creating sustainable ventures. It also covers business model development, market analysis, financial planning, leadership, risk management, pitching, and scaling entrepreneurial ideas.

This course introduces the fundamentals of Cloud Computing and Cloud Technologies, covering cloud service and deployment models, virtualization, cloud architecture, storage, networking, and major cloud platforms. Students learn cloud security principles, identity and access management, data protection, encryption, network security, shared responsibility model, and common cloud security threats. The course also covers cloud security frameworks, compliance, monitoring, risk management, secure cloud configuration, and best practices for designing and managing secure cloud environments.

This lab provides hands-on experience with cloud platforms, virtualization, cloud storage, networking, and basic cloud resource management using suitable cloud environments and tools. Students practice implementing identity and access management, security policies, encryption, network security controls, secure configurations, and monitoring mechanisms in controlled cloud environments. The lab develops practical skills in cloud security assessment, threat identification, access control, data protection, compliance, and applying security frameworks and best practices to cloud-based systems.

3RD SEMESTER SUBJECTS

This course introduces the fundamentals of Java programming and Object-Oriented Programming, including Java features, data types, operators, control statements, classes, objects, constructors, inheritance, polymorphism, interfaces, and packages.It further covers exception handling, multithreading, applets, AWT and event handling, and file I/O, enabling students to develop structured, interactive, and robust Java applications.

This practical course provides hands-on experience in Java programming and Object-Oriented Programming, covering classes and objects, inheritance, interfaces, packages, exception handling, and multithreading.It further develops practical skills in Java Applets, AWT GUI, event handling, and file handling, enabling students to design, implement, and execute interactive and file-based Java applications.

This course provides a comprehensive understanding of Data Structures and Algorithms, covering arrays, stacks, queues, linked lists, trees, graphs, and their fundamental operations and applications.It also focuses on algorithm analysis, time and space complexity, searching and sorting techniques, including Bubble, Insertion, Selection, Quick, Merge, Heap, Radix, Linear, and Binary Search.

This practical course provides hands-on experience in Data Structures and Algorithms, covering arrays, matrices, stacks, queues, linked lists, recursion, trees, and graphs with their fundamental operations and traversal techniques. It also focuses on implementing searching and sorting algorithms such as Linear Search, Binary Search, Bubble Sort, Selection Sort, Insertion Sort, and Merge Sort, along with infix-to-postfix conversion.

This course provides a comprehensive understanding of Computer Organization and Architecture, covering number systems, data representation, functional units, Von Neumann architecture, CPU organization, instruction cycles, addressing modes, and microoperations. It further explores I/O and memory organization, cache and DMA, pipelining, parallel processing, Flynn’s taxonomy, pipeline hazards, RISC architecture, and instruction-level parallelism.

This course provides a comprehensive understanding of Data Communication and Computer Networks, covering transmission techniques, communication modes, transmission media, multiplexing, network types, topologies, devices, OSI/TCP-IP models, and switching techniques. It further explores data link, network, transport, and application layers, including error control, Ethernet, IP addressing, routing, TCP/UDP, DNS, HTTP, FTP, SMTP, cryptography, firewalls, and basic network security.

This practical course provides hands-on experience in Computer Networking and Data Communication, covering network devices and cables, networking commands, topologies, OSI/TCP-IP models, IP addressing, subnetting, LAN configuration, and resource sharing. It further develops practical skills in application-layer protocols, DNS, HTTP/HTTPS, SMTP, TCP/UDP client-server communication, network security, firewalls, and basic cryptography using Python.

This course develops quantitative aptitude skills through Number System, Divisibility, Average, HCF & LCM, Percentage, Time & Distance, Time & Work, Ratio & Proportion, and Ages.It also covers logical reasoning and data interpretation including Blood Relations, Coding-Decoding, Direction Sense, Seating Arrangement, Syllogism, Bar Graphs, Pie Charts, Histograms, and Tabular Data.

This course develops effective communication, active listening, empathy, interpersonal skills, and basic counseling techniques for understanding and supporting individuals.

It also covers rapport building, problem identification, questioning techniques, confidentiality, conflict management, and ethical practices in counseling.

This course provides a comprehensive introduction to Data Science using Python, covering the data science landscape, methodology, cloud-based data science, and essential programming tools. Students will learn NumPy, Pandas, data preparation, statistical analysis, probability, and data visualization for extracting meaningful insights from data. The course further develops skills in machine learning, including regression, classification, clustering, PCA, and model evaluation. It also introduces Deep Learning, Generative AI, Large Language Models, Prompt Engineering, RAG, and AI Ethics for developing modern end-to-end data science solutions.

This laboratory course provides hands-on experience in Python programming, NumPy, Pandas, data cleaning, preprocessing, and exploratory data analysis. Students will perform statistical analysis and create visualizations using Matplotlib and Seaborn. The course also includes practical implementation of machine learning algorithms such as Linear Regression, Logistic Regression, and Decision Trees.

4TH SEMESTER SUBJECTS

This course introduces Python programming fundamentals, including variables, data types, operators, control structures, functions, modules, packages, and object-oriented programming. It also covers file handling, exception handling, regular expressions, NumPy, Pandas, and Matplotlib, enabling students to develop practical programs and perform basic data analysis and visualization.

This practical course provides hands-on experience in Python programming fundamentals, covering basic syntax, input/output, operators, strings, collections, decision-making, loops, functions, modules, and object-oriented programming. It further introduces inheritance, file handling, exception handling, regular expressions, and data visualization, enabling students to develop practical Python applications using libraries such as Pandas and Matplotlib.

This course provides a comprehensive understanding of network security principles, vulnerabilities, cryptography, wireless security, secure communication, and authentication mechanisms. It also covers access control, firewalls, IDS/IPS, PKI, secure protocols, and modern authentication technologies to develop skills for securing networked systems.

This practical course provides hands-on experience in network security configuration and testing, covering virtualization, DNSSEC, firewalls, ACLs, Wireshark, VPNs, wireless security, and authentication protocols.It also focuses on web security testing, endpoint protection, secure email protocols, and incident response, enabling students to identify, mitigate, and recover from simulated cyber attacks.

This course provides a comprehensive understanding of Operating System concepts, services, system calls, process management, CPU scheduling, threads, inter-process communication, and process synchronization. It also covers deadlocks, memory management, paging, segmentation, virtual memory, page replacement, disk scheduling, and storage reliability, enabling students to understand and manage system resources efficiently.

This practical course provides hands-on experience with VMware/VirtualBox, virtual machine creation and management, Linux installation and configuration, basic Linux commands, directory and file handling, and file permissions. It also covers Linux security, filters, Vi editor, and shell scripting, enabling students to develop practical skills in Linux system administration and automation.

This course covers fundamental concepts of Discrete Mathematics, including set theory, relations and functions, recursion, recurrence relations, and algebraic structures such as groups, rings, and fields. It also focuses on graph theory and trees, including graph properties, paths, circuits, shortest paths, spanning trees, and minimum spanning tree algorithms such as Kruskal’s and Prim’s algorithms.

This course provides a comprehensive understanding of DBMS concepts, data models, database design, ER modeling, relational algebra, SQL, advanced SQL, functional dependencies, and normalization. It also covers transaction management, concurrency control, recovery, database security, indexing, NoSQL databases, MongoDB, data warehousing, OLAP, and basic data mining concepts.

This practical course provides hands-on experience in RDBMS installation, database and schema design, ER modeling, DDL/DML commands, SQL queries, aggregate functions, joins, views, subqueries, procedures, functions, and triggers.It also covers normalization, transaction and concurrency control, database security and access control, and MongoDB, enabling students to develop and manage relational and NoSQL databases effectively.

This course develops quantitative aptitude and problem-solving skills through topics such as Profit & Loss, Time & Work, Time & Distance, Percentage, Ratio & Proportion, and Mensuration. It also covers Puzzles, Seating Arrangement, Clock & Calendar, Permutation & Combination, Simple & Compound Interest, Simplification, Mixture & Allegation, along with regular mock tests for competitive examinations.

This course explores human values and personal transformation through self-awareness, love, compassion, truth, non-violence, empathy, righteousness, sacrifice, and renunciation. It develops ethical thinking, emotional awareness, social responsibility, and inner transformation through reflective discussions, case studies, biographies, stories, debates, and self-analysis activities.

This course introduces the fundamentals of Artificial Intelligence, including its evolution, types, applications, and intelligent agents. It develops practical skills in Python programming using NumPy, Pandas, and Matplotlib for AI and data analysis. The course also covers Machine Learning, Deep Learning, CNNs, and hands-on chatbot development using IBM Watson Assistant.

This lab provides hands-on experience in Python programming for Artificial Intelligence, covering control structures, functions, NumPy, Pandas, and Matplotlib for numerical computation, data handling, and visualization. Students implement data preprocessing, feature scaling, and basic Machine Learning algorithms such as regression and classification, along with model evaluation using accuracy, precision, recall, and confusion matrix. The lab also introduces fundamental Deep Learning concepts and enables students to develop functional AI chatbots using IBM Watson Assistant and cloud-based tools.

5TH SEMESTER SUBJECTS

This course provides a comprehensive understanding of cloud computing concepts, including cloud paradigms, virtualization, migration, capacity planning, SLA management, security, and storage.It also introduces advanced cloud technologies such as energy-efficient computing, federated and mobile clouds, fog computing, Big Data, IoT, and industry cloud platforms.

This course provides a comprehensive understanding of data warehousing, OLAP/OLTP, data mining, data preprocessing, classification, clustering, regression, and association rule mining techniques. It also covers decision trees, Naïve Bayes, cluster analysis, search engine architecture and ranking, web content mining, text mining, and spatial-temporal data mining.

This course provides a comprehensive understanding of Artificial Intelligence, intelligent agents, problem-solving search techniques, knowledge representation, machine learning, data preprocessing, supervised and unsupervised learning, and model evaluation. It also covers neural networks, deep learning, NLP, computer vision, Generative AI, GANs, diffusion models, LLMs, prompt engineering, AI tools, ethics, responsible AI, and emerging trends.

This practical course provides hands-on experience with cloud services, SaaS/PaaS/IaaS, warehouse applications, virtualization, virtual machine management, and public cloud tools. It also covers cloud storage and security management, private cloud setup using OpenStack/Eucalyptus, VM deployment through OpenStack, and Hadoop single-node cluster with basic applications.

This practical course provides hands-on experience in Python for AI/ML, data handling, preprocessing, visualization, feature selection, regression, classification, clustering, and model evaluation using NumPy, Pandas, Matplotlib, and Scikit-learn.

It also covers Decision Trees, Random Forest, neural networks, model performance analysis, and an end-to-end AI/ML mini project using real-world datasets.

This course provides a comprehensive understanding of research methodology, research design, literature review, hypothesis formulation, data collection, sampling methods, and statistical data analysis. It also covers hypothesis testing, correlation and regression, ANOVA, multivariate analysis, reliability, validity, and research report writing.

This course provides practical exposure to industry-oriented skills, professional practices, tools, technologies, and real-world problem-solving through hands-on training. Students gain experience in applying theoretical knowledge, completing practical tasks/projects, developing professional competencies, and preparing training reports and presentations.

This course provides comprehensive knowledge of Linux and RHEL, command-line operations, file management, user and system administration, storage, networking, SSH, and server configuration. It also covers Linux security, firewall and SELinux, web/file services, shell scripting, system monitoring, troubleshooting, automation, virtualization, and containers.

This course introduces the fundamentals of Deep Learning and Artificial Neural Networks, covering perceptrons, multilayer networks, activation functions, loss functions, optimization, and backpropagation.Students learn Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTM/GRU networks, autoencoders, and transfer learning for processing images, text, and sequential data.The course also covers deep learning frameworks, model training and evaluation, regularization, hyperparameter tuning, and applications of deep learning in real-world problems.

This lab provides hands-on experience in implementing neural networks and deep learning models using Python and frameworks such as TensorFlow/Keras or PyTorch.Students develop and train CNN, RNN, LSTM, autoencoder, and transfer learning models for image classification, sequence processing, and other machine learning tasks.The lab develops practical skills in data preprocessing, model training, evaluation, visualization, hyperparameter tuning, and performance optimization for real-world deep learning applications.

This course focuses on applying academic knowledge, technical skills, problem-solving, and research methodologies to identify and address a real-world problem through a project. Students work on problem identification, requirement analysis, project planning, design, implementation, testing, documentation, and presentation of project outcomes.

6TH SEMESTER SUBJECTS

This course provides a comprehensive understanding of computer graphics, scan-conversion algorithms, 2D/3D transformations, viewing, clipping, projections, and geometric modeling techniques. It also covers 3D rendering, polygon meshes, curves, visible surface detection, illumination, shading, color models, morphing, and computer animation.

This course provides comprehensive knowledge of data visualization, business intelligence, Power BI, data sources, Power Query, data cleaning, transformation, and data modelling using DAX. It also covers interactive dashboards, advanced visualizations, data storytelling, report publishing, sharing, security, real-time analytics, and business intelligence case studies.

This course provides a comprehensive understanding of software development approaches, software testing principles, SDLC/STLC, testing techniques, verification, validation, and software quality assurance. It also covers white-box and black-box testing, quality standards, configuration and change management, debugging techniques, IDE tools, exception handling, and testing best practices.

This practical course provides hands-on experience in computer graphics algorithms, primitive drawing, 2D/3D transformations, clipping, projections, color palettes, shading, and surface visibility techniques. It also develops skills in** implementing graphics algorithms and creating interactive applications through a minor project such as a game or animation.**

This practical course provides hands-on experience in SDLC/STLC, test case design, unit, integration and system testing, error identification, and bug reporting for software applications. It also covers software quality parameters, quality checklists, test planning, and preparation of test cases through a practical mini project.

This practical course provides hands-on experience in Power BI data loading, cleaning, transformation, visualization, Power Query, DAX functions, and interactive report development. It also covers charts, tables, matrices, cards, filters, slicers, dashboard design, and an end-to-end interactive dashboard mini project.

This course focuses on applying advanced technical knowledge, problem-solving, research, and project development skills to address a real-world problem.

Students undertake an independent capstone project involving requirement analysis, design, implementation, testing, documentation, and final presentation of the project outcomes.

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.