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Fees
₹2,50,000
Placement
92.5%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Fees
₹2,50,000
Placement
92.5%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Seats
100
Students
300
Seats
100
Students
300
The Computer Science program at Balwant Singh Mukhiya Bsm College Of Polytechnic is meticulously structured to provide a balanced mix of theoretical knowledge and practical application. The curriculum spans eight semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions designed to enhance hands-on learning.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Mathematics for Computer Science | 3-1-0-4 | - |
| 1 | CS102 | Introduction to Programming | 3-0-2-5 | - |
| 1 | CS103 | Digital Logic Design | 3-0-2-5 | - |
| 1 | CS104 | Engineering Graphics and Design | 2-0-2-4 | - |
| 1 | CS105 | English Communication Skills | 2-0-0-2 | - |
| 1 | SC101 | Physics for Computer Science | 3-1-0-4 | - |
| 1 | SC102 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | SC103 | Biology for Computer Science | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS102 |
| 2 | CS202 | Object-Oriented Programming | 3-0-2-5 | CS102 |
| 2 | CS203 | Database Management Systems | 3-1-0-4 | CS102 |
| 2 | CS204 | Computer Networks | 3-1-0-4 | CS103 |
| 2 | CS205 | Operating Systems | 3-1-0-4 | CS201 |
| 2 | SC201 | Statistics and Probability | 3-1-0-4 | CS101 |
| 2 | SC202 | Linear Algebra and Calculus | 3-1-0-4 | CS101 |
| 3 | CS301 | Design and Analysis of Algorithms | 3-1-0-4 | CS201 |
| 3 | CS302 | Software Engineering | 3-1-0-4 | CS202 |
| 3 | CS303 | Computer Architecture | 3-1-0-4 | CS103 |
| 3 | CS304 | Artificial Intelligence | 3-1-0-4 | CS201 |
| 3 | CS305 | Web Technologies | 3-0-2-5 | CS202 |
| 3 | SC301 | Probability and Stochastic Processes | 3-1-0-4 | SC201 |
| 4 | CS401 | Machine Learning | 3-1-0-4 | CS301 |
| 4 | CS402 | Cybersecurity | 3-1-0-4 | CS204 |
| 4 | CS403 | Data Mining and Warehousing | 3-1-0-4 | CS301 |
| 4 | CS404 | Cloud Computing | 3-1-0-4 | CS204 |
| 4 | CS405 | Mobile Application Development | 3-0-2-5 | CS202 |
| 4 | SC401 | Advanced Mathematics for CS | 3-1-0-4 | SC202 |
| 5 | CS501 | Deep Learning | 3-1-0-4 | CS401 |
| 5 | CS502 | Human Computer Interaction | 3-1-0-4 | CS302 |
| 5 | CS503 | Database Security | 3-1-0-4 | CS203 |
| 5 | CS504 | Internet of Things | 3-1-0-4 | CS204 |
| 5 | CS505 | Game Development | 3-0-2-5 | CS202 |
| 5 | SC501 | Optimization Techniques | 3-1-0-4 | SC401 |
| 6 | CS601 | Reinforcement Learning | 3-1-0-4 | CS501 |
| 6 | CS602 | Quantitative Finance | 3-1-0-4 | SC301 |
| 6 | CS603 | Advanced Cryptography | 3-1-0-4 | CS204 |
| 6 | CS604 | Embedded Systems | 3-1-0-4 | CS303 |
| 6 | CS605 | Computer Vision | 3-1-0-4 | CS401 |
| 6 | SC601 | Advanced Probability Models | 3-1-0-4 | SC501 |
| 7 | CS701 | Research Methodology | 2-0-0-2 | - |
| 7 | CS702 | Capstone Project - Phase 1 | 3-0-6-9 | - |
| 8 | CS801 | Capstone Project - Phase 2 | 4-0-8-12 | CS702 |
Departmental electives in the Computer Science program are carefully curated to offer depth and specialization beyond core requirements. These courses aim to deepen students' understanding of specific areas while providing advanced technical skills and research exposure.
This course provides a comprehensive introduction to machine learning techniques, including supervised and unsupervised learning, neural networks, and reinforcement learning. Students will learn to implement algorithms using Python libraries like scikit-learn and TensorFlow.
The cybersecurity elective covers essential topics such as network security protocols, encryption techniques, ethical hacking, and digital forensics. It prepares students for careers in securing digital assets and managing cyber threats.
This course explores advanced concepts in database security, including access control, auditing, and privacy-preserving techniques. Students will gain expertise in protecting sensitive data through secure design principles.
The IoT elective introduces students to the architecture and protocols used in connected devices. It covers sensor networks, cloud integration, and real-time data processing in smart environments.
This course focuses on creating interactive entertainment software using modern game engines. Students will learn game design principles, scripting languages, and visual effects implementation.
The deep learning course delves into neural network architectures and their applications in image recognition, natural language processing, and generative models. Students will build and train complex deep learning models using frameworks like PyTorch and TensorFlow.
This elective covers the fundamentals of computer vision, including image processing, feature extraction, object detection, and tracking. Students will apply these techniques in practical scenarios involving autonomous vehicles and medical imaging.
The quantitative finance course introduces mathematical models used in financial markets, including derivatives pricing, risk management, and algorithmic trading strategies. It combines financial theory with computational methods.
This advanced elective explores modern cryptographic techniques, including public-key cryptography, hash functions, and blockchain technologies. Students will study both theoretical foundations and practical implementations.
The embedded systems course focuses on designing and developing software for resource-constrained devices. It covers microcontroller programming, real-time operating systems, and hardware-software integration techniques.
Our department strongly believes in project-based learning as a cornerstone of effective education. Projects provide students with the opportunity to apply theoretical knowledge to real-world problems, fostering creativity, teamwork, and innovation.
The curriculum includes mandatory mini-projects in the second year, followed by a final-year thesis or capstone project. These projects are designed to be challenging yet achievable, allowing students to explore their interests while developing essential skills.
Mini-projects typically span one semester and involve small teams working on specific technical challenges. Students must present their findings at the end of the term and receive feedback from faculty members.
The final-year capstone project is a significant undertaking that spans both semesters of the eighth year. Students select projects based on their interests, aligning them with faculty expertise and industry needs. They work closely with assigned mentors to develop innovative solutions or conduct original research.
Evaluation criteria for projects include technical proficiency, innovation, presentation quality, and team collaboration. Regular progress reviews ensure that students stay on track and receive timely support when needed.
