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Fees
₹8,50,000
Placement
92.5%
Avg Package
₹6,00,000
Highest Package
₹15,00,000
Fees
₹8,50,000
Placement
92.5%
Avg Package
₹6,00,000
Highest Package
₹15,00,000
Seats
120
Students
350
Seats
120
Students
350
The Computer Applications program at Guru Kashi University Bathinda is meticulously structured to provide a balanced blend of theoretical knowledge and practical application. The curriculum spans four academic years, with each year divided into two semesters, totaling eight semesters.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| I | CS102 | Physics for Computer Science | 3-1-0-4 | - |
| I | CS103 | Chemistry for Computing | 3-1-0-4 | - |
| I | CS104 | Introduction to Programming Using C | 2-0-2-3 | - |
| I | CS105 | English Communication Skills | 2-0-0-2 | - |
| I | CS106 | Workshop in Programming | 0-0-4-2 | - |
| II | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| II | CS202 | Object Oriented Programming using C++ | 2-0-2-3 | CS104 |
| II | CS203 | Data Structures and Algorithms | 3-1-0-4 | CS104 |
| II | CS204 | Computer Organization and Architecture | 3-1-0-4 | - |
| II | CS205 | Database Management Systems | 3-1-0-4 | CS104 |
| II | CS206 | Discrete Mathematical Structures | 3-1-0-4 | CS101 |
| III | CS301 | Operating Systems | 3-1-0-4 | CS202, CS204 |
| III | CS302 | Computer Networks | 3-1-0-4 | CS204 |
| III | CS303 | Software Engineering | 3-1-0-4 | CS202, CS203 |
| III | CS304 | Web Technologies | 2-0-2-3 | CS202 |
| III | CS305 | Probability and Statistics | 3-1-0-4 | CS101 |
| III | CS306 | Elective I - Data Structures & Algorithms | 2-0-2-3 | CS203 |
| IV | CS401 | Compiler Design | 3-1-0-4 | CS301, CS302 |
| IV | CS402 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS203, CS305 |
| IV | CS403 | Cybersecurity Fundamentals | 3-1-0-4 | CS302 |
| IV | CS404 | Mobile Application Development | 2-0-2-3 | CS202, CS304 |
| IV | CS405 | Cloud Computing | 3-1-0-4 | CS302 |
| IV | CS406 | Elective II - Network Security | 2-0-2-3 | CS302 |
| V | CS501 | Advanced Data Structures and Algorithms | 3-1-0-4 | CS203 |
| V | CS502 | Big Data Analytics | 3-1-0-4 | CS305, CS501 |
| V | CS503 | Internet of Things (IoT) | 3-1-0-4 | CS204 |
| V | CS504 | Blockchain Technology | 3-1-0-4 | - |
| V | CS505 | Human Computer Interaction | 2-0-2-3 | - |
| V | CS506 | Elective III - Machine Learning | 2-0-2-3 | CS402 |
| VI | CS601 | Distributed Systems | 3-1-0-4 | CS301, CS302 |
| VI | CS602 | Software Testing and Quality Assurance | 3-1-0-4 | CS303 |
| VI | CS603 | Quantum Computing | 3-1-0-4 | - |
| VI | CS604 | Research Methodology | 2-0-2-3 | - |
| VI | CS605 | Project Management | 2-0-2-3 | - |
| VI | CS606 | Elective IV - Software Architecture | 2-0-2-3 | CS303 |
| VII | CS701 | Thesis Proposal | 0-0-6-4 | - |
| VIII | CS801 | Final Year Project | 0-0-12-8 | CS701 |
The department offers a rich selection of advanced elective courses designed to provide students with specialized knowledge and skills in emerging areas of computer science.
Artificial Intelligence and Machine Learning: This course explores the principles and applications of AI, covering topics such as neural networks, deep learning, reinforcement learning, natural language processing, and computer vision. Students learn how to design and implement intelligent systems using frameworks like TensorFlow and PyTorch.
Cybersecurity Fundamentals: This elective introduces students to the fundamentals of cybersecurity, including network security, cryptography, ethical hacking, and digital forensics. Students gain hands-on experience with security tools and techniques used in real-world scenarios.
Cloud Computing: The course covers cloud architecture, virtualization technologies, containerization, and microservices design patterns. Students learn how to deploy scalable applications on platforms like AWS, Azure, and Google Cloud.
Mobile Application Development: This course focuses on building cross-platform mobile applications using modern frameworks such as React Native and Flutter. Students develop skills in UI/UX design, API integration, and mobile security.
Data Science and Analytics: The course covers statistical modeling, data visualization, predictive analytics, and big data technologies. Students learn how to extract insights from complex datasets using Python and R.
Internet of Things (IoT): This elective explores sensor networks, embedded systems, smart devices, and real-time data processing. Students gain practical experience in designing and implementing IoT solutions for various industries.
Human-Computer Interaction: The course focuses on user experience design, usability testing, and interaction prototyping. Students learn how to create intuitive interfaces that enhance user satisfaction.
Blockchain Technology: This elective covers decentralized ledger technologies, smart contracts, cryptocurrency mining, and distributed consensus mechanisms. Students understand both the technical and regulatory aspects of blockchain applications.
Quantum Computing: The course introduces quantum algorithms, quantum gates, and quantum programming using Qiskit and Cirq. Students explore potential applications of quantum computing in cryptography and optimization.
Software Testing and Quality Assurance: This course covers software testing methodologies, automation tools, and quality assurance practices. Students learn how to ensure the reliability and performance of software products.
The department emphasizes project-based learning as a core component of its curriculum. Projects are designed to bridge the gap between theory and practice, allowing students to apply their knowledge to real-world problems.
Mini-projects are undertaken in the third and fourth semesters, focusing on specific topics such as web development, database design, or algorithm implementation. These projects are typically completed in teams and involve regular milestones and peer reviews.
The final-year thesis/capstone project is a significant component of the program. Students select a research topic under faculty supervision, conduct original research, and present their findings to an evaluation committee. The project involves literature review, experimental design, data analysis, and documentation.
Students can choose from a list of proposed projects or propose their own ideas after consultation with faculty mentors. The selection process ensures that students work on topics aligned with their interests and career goals while maintaining academic rigor.
