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Fees
₹5,00,000
Placement
92.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Fees
₹5,00,000
Placement
92.0%
Avg Package
₹7,50,000
Highest Package
₹18,00,000
Seats
300
Students
1,200
Seats
300
Students
1,200
The Computer Science program at Isbm University Gariyaband is structured over eight semesters, providing a comprehensive and progressive learning experience. Each semester builds upon the previous one to ensure students develop both theoretical knowledge and practical skills.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| Semester I | CS101 | Introduction to Computing | 3-0-0-3 | None |
| CS102 | Computer Programming Fundamentals | 3-0-0-3 | None | |
| MA101 | Calculus I | 4-0-0-4 | None | |
| PH101 | Physics for Engineers | 3-0-0-3 | None | |
| CH101 | Chemistry for Engineers | 3-0-0-3 | None | |
| EG101 | Engineering Graphics | 2-0-0-2 | None | |
| HS101 | English for Communication | 3-0-0-3 | None | |
| ME101 | Introduction to Mechanical Engineering | 2-0-0-2 | None | |
| CS103 | Programming Lab (Python) | 0-0-3-1 | CS102 | |
| PH102 | Physics Lab | 0-0-3-1 | PH101 | |
| Semester II | CS201 | Data Structures & Algorithms | 3-0-0-3 | CS102 |
| CS202 | Database Management Systems | 3-0-0-3 | CS102 | |
| MA201 | Calculus II | 4-0-0-4 | MA101 | |
| PH201 | Electromagnetism and Optics | 3-0-0-3 | PH101 | |
| CH201 | Organic Chemistry | 3-0-0-3 | CH101 | |
| EG201 | Engineering Mechanics | 3-0-0-3 | ME101 | |
| HS201 | Communication Skills | 3-0-0-3 | HS101 | |
| ME201 | Thermodynamics | 3-0-0-3 | ME101 | |
| CS203 | Data Structures Lab | 0-0-3-1 | CS201 | |
| PH202 | Electronics Lab | 0-0-3-1 | PH201 | |
| Semester III | CS301 | Operating Systems | 3-0-0-3 | CS201 |
| CS302 | Computer Networks | 3-0-0-3 | CS201 | |
| MA301 | Probability and Statistics | 3-0-0-3 | MA201 | |
| PH301 | Quantum Physics | 3-0-0-3 | PH201 | |
| CH301 | Inorganic Chemistry | 3-0-0-3 | CH201 | |
| EG301 | Electrical Circuits | 3-0-0-3 | EG201 | |
| HS301 | Business Communication | 3-0-0-3 | HS201 | |
| ME301 | Fluid Mechanics | 3-0-0-3 | ME201 | |
| CS303 | Operating Systems Lab | 0-0-3-1 | CS301 | |
| CS304 | Networks Lab | 0-0-3-1 | CS302 | |
| Semester IV | CS401 | Software Engineering | 3-0-0-3 | CS201 |
| CS402 | Web Technologies | 3-0-0-3 | CS201 | |
| MA401 | Linear Algebra | 3-0-0-3 | MA201 | |
| PH401 | Modern Physics | 3-0-0-3 | PH301 | |
| CH401 | Physical Chemistry | 3-0-0-3 | CH301 | |
| EG401 | Control Systems | 3-0-0-3 | EG301 | |
| HS401 | Leadership and Teamwork | 3-0-0-3 | HS301 | |
| ME401 | Mechanics of Materials | 3-0-0-3 | ME301 | |
| CS403 | Software Engineering Lab | 0-0-3-1 | CS401 | |
| CS404 | Web Technologies Lab | 0-0-3-1 | CS402 | |
| Semester V | CS501 | Machine Learning | 3-0-0-3 | CS201, MA301 |
| CS502 | Cybersecurity Fundamentals | 3-0-0-3 | CS302 | |
| MA501 | Differential Equations | 3-0-0-3 | MA401 | |
| PH501 | Nuclear Physics | 3-0-0-3 | PH401 | |
| CH501 | Chemical Engineering Principles | 3-0-0-3 | CH401 | |
| EG501 | Digital Electronics | 3-0-0-3 | EG301 | |
| HS501 | Entrepreneurship and Innovation | 3-0-0-3 | HS401 | |
| ME501 | Design of Machine Elements | 3-0-0-3 | ME401 | |
| CS503 | ML Lab | 0-0-3-1 | CS501 | |
| CS504 | Cybersecurity Lab | 0-0-3-1 | CS502 | |
| Semester VI | CS601 | Data Analytics | 3-0-0-3 | CS201, MA301 |
| CS602 | Advanced Computer Architecture | 3-0-0-3 | CS301 | |
| MA601 | Complex Analysis | 3-0-0-3 | MA501 | |
| PH601 | Optics and Lasers | 3-0-0-3 | PH501 | |
| CH601 | Industrial Chemistry | 3-0-0-3 | CH501 | |
| EG601 | Microprocessors and Microcontrollers | 3-0-0-3 | EG501 | |
| HS601 | Global Business Environment | 3-0-0-3 | HS501 | |
| ME601 | Mechanical Design | 3-0-0-3 | ME501 | |
| CS603 | Data Analytics Lab | 0-0-3-1 | CS601 | |
| CS604 | Architecture Lab | 0-0-3-1 | CS602 | |
| Semester VII | CS701 | Computer Vision | 3-0-0-3 | CS501, CS201 |
| CS702 | Reinforcement Learning | 3-0-0-3 | CS501 | |
| MA701 | Topology | 3-0-0-3 | MA601 | |
| PH701 | Quantum Computing | 3-0-0-3 | PH501 | |
| CH701 | Pharmaceutical Chemistry | 3-0-0-3 | CH601 | |
| EG701 | Embedded Systems | 3-0-0-3 | EG601 | |
| HS701 | Corporate Governance | 3-0-0-3 | HS601 | |
| ME701 | Advanced Manufacturing | 3-0-0-3 | ME601 | |
| CS703 | Computer Vision Lab | 0-0-3-1 | CS701 | |
| CS704 | Reinforcement Learning Lab | 0-0-3-1 | CS702 | |
| Semester VIII | CS801 | Capstone Project I | 3-0-0-3 | All previous semesters |
| CS802 | Capstone Project II | 3-0-0-3 | CS801 | |
| MA801 | Advanced Numerical Methods | 3-0-0-3 | MA701 | |
| PH801 | Condensed Matter Physics | 3-0-0-3 | PH701 | |
| CH801 | Biochemistry | 3-0-0-3 | CH701 | |
| EG801 | Advanced Signal Processing | 3-0-0-3 | EG701 | |
| HS801 | Strategic Management | 3-0-0-3 | HS701 | |
| ME801 | Robotics and Automation | 3-0-0-3 | ME701 | |
| CS803 | Capstone Lab I | 0-0-3-1 | CS801 | |
| CS804 | Capstone Lab II | 0-0-3-1 | CS802 |
The department offers a range of advanced departmental electives that allow students to explore specialized areas within Computer Science. These courses are designed to deepen understanding and provide practical experience in emerging technologies.
This course delves into advanced topics in machine learning, including deep learning architectures, reinforcement learning, natural language processing, and computer vision. Students learn to implement complex models using frameworks like TensorFlow and PyTorch, while also exploring ethical considerations and real-world applications.
This elective focuses on cloud infrastructure, virtualization, containerization, and distributed computing models. Students study public and private cloud platforms, including AWS, Azure, and Google Cloud, learning how to design scalable and secure cloud-based solutions for enterprise environments.
This course explores modern software architecture patterns, microservices, event-driven systems, and cloud-native applications. Students learn about system design principles, scalability challenges, and best practices for building robust and maintainable software systems.
This elective provides a comprehensive overview of cryptographic algorithms, network security protocols, and secure communication systems. Students study encryption techniques, digital signatures, authentication mechanisms, and penetration testing methods used to protect sensitive data in modern networks.
This course examines the design and evaluation of interactive computing systems. Students learn about user experience principles, interface design, usability testing, and accessibility considerations. The course includes hands-on projects where students prototype interfaces and conduct user research to improve system usability.
This elective covers advanced database concepts, including transaction management, indexing techniques, query optimization, and distributed databases. Students learn to design and implement large-scale database systems using SQL and NoSQL technologies, with a focus on performance tuning and data integrity.
This course teaches students how to develop mobile applications for Android and iOS platforms. Topics include mobile UI/UX design, native and cross-platform development frameworks, app deployment strategies, and integration with backend services. Students build full-stack mobile applications from concept to release.
This elective explores the architecture, protocols, and applications of IoT systems. Students study sensor networks, embedded programming, wireless communication standards, and cloud integration for IoT devices. The course includes lab work with Raspberry Pi, Arduino, and other IoT platforms.
This course covers the fundamentals of computer graphics, including rendering techniques, 3D modeling, animation, and visualization methods. Students learn to develop graphics applications using OpenGL, DirectX, and modern graphics APIs, with projects involving real-time rendering and interactive visualizations.
This elective introduces students to big data processing frameworks such as Hadoop, Spark, and Kafka. Students learn to process large datasets, perform distributed computing tasks, and analyze streaming data using various tools and platforms. The course includes hands-on experience with real-world datasets and enterprise-scale applications.
This advanced course explores the principles of quantum mechanics and their application in computing. Students study quantum algorithms, error correction, and quantum programming languages like Qiskit and Cirq. The course includes theoretical concepts and practical implementations using quantum simulators and real quantum hardware.
This course covers the theory and practice of reinforcement learning, including Markov decision processes, Q-learning, policy gradients, and deep reinforcement learning. Students implement agents that learn optimal behaviors through interaction with environments, applying these techniques to robotics, game playing, and autonomous systems.
This elective focuses on image processing, object detection, segmentation, and recognition using machine learning and deep learning techniques. Students study convolutional neural networks, feature extraction, and real-time computer vision applications in surveillance, medical imaging, and augmented reality.
This course explores text analysis, language modeling, sentiment analysis, and machine translation using NLP techniques. Students work with large text corpora, build language models, and develop systems for automated summarization, question answering, and dialogue generation.
This elective covers the design and implementation of embedded systems for real-time applications. Students learn about microcontrollers, real-time operating systems, hardware-software co-design, and low-power optimization techniques. Projects include building autonomous robots, smart home devices, and industrial control systems.
This course focuses on software quality assurance, testing methodologies, automation frameworks, and defect management. Students learn to design test cases, implement automated tests, and ensure software reliability through continuous integration and deployment practices.
The Computer Science program at Isbm University Gariyaband places a strong emphasis on project-based learning as the cornerstone of student development. This approach ensures that students not only understand theoretical concepts but also gain hands-on experience in solving real-world problems.
Throughout the program, students are required to complete several mini-projects that align with course objectives and build upon previously learned skills. These projects are typically completed in groups of 3-5 students and span a period of 2-3 weeks. Each project has specific learning outcomes, deliverables, and evaluation criteria.
The capstone project is the culmination of the student's academic journey and serves as a comprehensive demonstration of their abilities in problem-solving, research, and technical implementation. Students select a topic under the guidance of a faculty mentor and work on it for an entire semester.
Students begin by identifying potential research topics during their third year. They may propose original ideas or choose from suggested projects provided by faculty members. The selection process involves submitting a project proposal, which is reviewed and approved by the academic committee.
Each student is assigned a faculty mentor who guides them through the research and implementation phases. Mentors provide regular feedback, suggest resources, and help students overcome technical challenges. The mentorship extends beyond project completion to include career guidance and academic support.
The final project is evaluated based on multiple criteria including innovation, technical depth, documentation quality, presentation skills, and the ability to work collaboratively. Students present their projects in front of a panel of faculty members and industry experts, receiving constructive feedback that helps improve their overall skillset.
