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Fees
₹5,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹9,00,000
Fees
₹5,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹9,00,000
Seats
120
Students
2,000
Seats
120
Students
2,000
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| Semester I | CS101 | Programming Fundamentals | 3-0-0-3 | None |
| MA101 | Mathematics for Computing I | 3-0-0-3 | None | |
| PH101 | Physics for Engineers | 3-0-0-3 | None | |
| CH101 | Chemistry for Engineers | 3-0-0-3 | None | |
| EC101 | Electrical Engineering Fundamentals | 3-0-0-3 | None | |
| HS101 | English Communication Skills | 2-0-0-2 | None | |
| ES101 | Engineering Graphics & Design | 2-0-2-4 | None | |
| CS102 | Computer Organization | 3-0-0-3 | CS101 | |
| MA102 | Mathematics for Computing II | 3-0-0-3 | MA101 | |
| PH102 | Modern Physics | 3-0-0-3 | PH101 | |
| CH102 | Organic Chemistry | 3-0-0-3 | CH101 | |
| CS103 | Data Structures and Algorithms | 3-0-0-3 | CS101 | |
| Semester II | CS201 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| MA201 | Statistics and Probability | 3-0-0-3 | MA101 | |
| PH201 | Optics and Electromagnetic Waves | 3-0-0-3 | PH101 | |
| CH201 | Inorganic Chemistry | 3-0-0-3 | CH101 | |
| EC201 | Digital Electronics | 3-0-0-3 | EC101 | |
| HS201 | Critical Thinking and Ethics | 2-0-0-2 | None | |
| ES201 | Design and Analysis of Algorithms | 3-0-0-3 | CS103 | |
| CS202 | Database Management Systems | 3-0-0-3 | CS103 | |
| MA202 | Linear Algebra and Calculus | 3-0-0-3 | MA102 | |
| PH202 | Quantum Physics | 3-0-0-3 | PH102 | |
| CH202 | Physical Chemistry | 3-0-0-3 | CH102 | |
| CS203 | Operating Systems | 3-0-0-3 | CS102 | |
| Semester III | CS301 | Computer Networks | 3-0-0-3 | CS102 |
| MA301 | Numerical Methods and Optimization | 3-0-0-3 | MA201 | |
| PH301 | Thermodynamics and Statistical Mechanics | 3-0-0-3 | PH201 | |
| CH301 | Chemical Engineering Fundamentals | 3-0-0-3 | CH201 | |
| EC301 | Signals and Systems | 3-0-0-3 | EC201 | |
| HS301 | Leadership and Team Management | 2-0-0-2 | None | |
| ES301 | Web Technologies | 3-0-0-3 | CS201 | |
| CS302 | Software Engineering | 3-0-0-3 | CS201 | |
| MA302 | Probability and Stochastic Processes | 3-0-0-3 | MA201 | |
| PH302 | Modern Physics Applications | 3-0-0-3 | PH202 | |
| CH302 | Industrial Chemistry | 3-0-0-3 | CH202 | |
| CS303 | Artificial Intelligence | 3-0-0-3 | CS103 | |
| Semester IV | CS401 | Cybersecurity Fundamentals | 3-0-0-3 | CS301 |
| MA401 | Mathematical Modeling | 3-0-0-3 | MA301 | |
| PH401 | Nuclear Physics and Applications | 3-0-0-3 | PH301 | |
| CH401 | Environmental Chemistry | 3-0-0-3 | CH301 | |
| EC401 | Control Systems | 3-0-0-3 | EC301 | |
| HS401 | Global Business Environment | 2-0-0-2 | None | |
| ES401 | Mobile Application Development | 3-0-0-3 | CS201 | |
| CS402 | Data Mining and Analytics | 3-0-0-3 | CS303 | |
| MA402 | Advanced Calculus and Differential Equations | 3-0-0-3 | MA202 | |
| PH402 | Quantum Mechanics Applications | 3-0-0-3 | PH302 | |
| CH402 | Materials Science and Engineering | 3-0-0-3 | CH302 | |
| CS403 | Cloud Computing | 3-0-0-3 | CS301 | |
| Semester V | CS501 | Machine Learning and Deep Learning | 3-0-0-3 | CS403 |
| MA501 | Operations Research | 3-0-0-3 | MA401 | |
| PH501 | Advanced Electromagnetism | 3-0-0-3 | PH401 | |
| CH501 | Pharmaceutical Chemistry | 3-0-0-3 | CH401 | |
| EC501 | Signal Processing | 3-0-0-3 | EC401 | |
| HS501 | Sustainable Development and Green Technologies | 2-0-0-2 | None | |
| ES501 | Internet of Things (IoT) | 3-0-0-3 | CS301 | |
| CS502 | Big Data Technologies | 3-0-0-3 | CS402 | |
| MA502 | Statistical Inference | 3-0-0-3 | MA302 | |
| PH502 | Optics and Lasers | 3-0-0-3 | PH402 | |
| CH502 | Biochemistry and Molecular Biology | 3-0-0-3 | CH402 | |
| CS503 | Blockchain Technologies | 3-0-0-3 | CS403 | |
| Semester VI | CS601 | Advanced Cybersecurity Techniques | 3-0-0-3 | CS401 |
| MA601 | Computational Mathematics | 3-0-0-3 | MA501 | |
| PH601 | Quantum Computing | 3-0-0-3 | PH501 | |
| CH601 | Industrial Biotechnology | 3-0-0-3 | CH501 | |
| EC601 | Wireless Communication Systems | 3-0-0-3 | EC501 | |
| HS601 | Entrepreneurship and Innovation | 2-0-0-2 | None | |
| ES601 | Advanced Web Development | 3-0-0-3 | CS302 | |
| CS602 | Computer Vision and Image Processing | 3-0-0-3 | CS501 | |
| MA602 | Time Series Analysis | 3-0-0-3 | MA502 | |
| PH602 | Condensed Matter Physics | 3-0-0-3 | PH502 | |
| CH602 | Pharmaceutical Manufacturing | 3-0-0-3 | CH502 | |
| CS603 | Research Methodology and Ethics | 3-0-0-3 | None | |
| Semester VII | CS701 | Advanced AI and Robotics | 3-0-0-3 | CS501 |
| MA701 | Financial Mathematics | 3-0-0-3 | MA601 | |
| PH701 | Advanced Quantum Physics | 3-0-0-3 | PH601 | |
| CH701 | Green Chemistry and Sustainability | 3-0-0-3 | CH601 | |
| EC701 | Advanced Control Systems | 3-0-0-3 | EC601 | |
| HS701 | Strategic Management and Leadership | 2-0-0-2 | None | |
| ES701 | Augmented Reality (AR) Development | 3-0-0-3 | CS602 | |
| CS702 | Natural Language Processing | 3-0-0-3 | CS501 | |
| MA702 | Statistical Machine Learning | 3-0-0-3 | MA602 | |
| PH702 | Quantum Field Theory | 3-0-0-3 | PH602 | |
| CH702 | Biochemical Engineering | 3-0-0-3 | CH602 | |
| CS703 | Capstone Project | 3-0-0-3 | CS603 | |
| Semester VIII | CS801 | Special Topics in Computer Science | 3-0-0-3 | CS701 |
| MA801 | Advanced Probability Theory | 3-0-0-3 | MA701 | |
| PH801 | Particle Physics | 3-0-0-3 | PH701 | |
| CH801 | Industrial Chemistry and Materials | 3-0-0-3 | CH701 | |
| EC801 | Optical Communication Systems | 3-0-0-3 | EC701 | |
| HS801 | Global Governance and Policy Making | 2-0-0-2 | None | |
| ES801 | Advanced Mobile Applications | 3-0-0-3 | CS602 | |
| CS802 | Deep Reinforcement Learning | 3-0-0-3 | CS701 | |
| MA802 | Bayesian Statistics | 3-0-0-3 | MA702 | |
| PH802 | String Theory and Cosmology | 3-0-0-3 | PH702 | |
| CH802 | Pharmaceutical Development and Quality Control | 3-0-0-3 | CH702 | |
| CS803 | Thesis/Research Project | 3-0-0-3 | CS703 |
Departmental electives play a pivotal role in shaping the academic and professional trajectory of students. They provide opportunities to explore specialized areas within Computer Applications, allowing students to tailor their learning experience based on personal interests and career goals.
This elective course introduces students to advanced techniques in machine learning using the popular TensorFlow framework. Students learn about neural networks, deep learning architectures, and how to implement models for image recognition, natural language processing, and recommendation systems.
This course delves into the methods and technologies used to enable computers to understand and generate human language. Topics include tokenization, sentiment analysis, language modeling, and building chatbots using transformer architectures.
Students explore how computers can interpret and analyze visual information from images and videos. The course covers convolutional neural networks (CNNs), object detection algorithms, and applications in surveillance, medical imaging, and autonomous vehicles.
This elective focuses on extracting meaningful patterns from large datasets using statistical techniques and machine learning algorithms. Students learn about clustering, classification, association rule mining, and data visualization tools such as Tableau and Power BI.
Designed to equip students with knowledge of modern big data processing frameworks like Hadoop, Spark, and Kafka. The course covers distributed computing models, real-time streaming analytics, and storage solutions for handling massive volumes of unstructured data.
This course explores the fundamentals of blockchain technology, smart contracts, and decentralized applications (dApps). Students learn to develop secure, transparent systems using Ethereum, Hyperledger Fabric, and other platforms while understanding regulatory implications.
Students are introduced to cloud platforms such as AWS, Azure, and Google Cloud. The course covers infrastructure as code (IaC), containerization with Docker, CI/CD pipelines, and microservices architecture in scalable environments.
This elective teaches students how to design, implement, and deploy IoT solutions using various sensors, microcontrollers, and communication protocols. Practical labs include developing smart home systems, environmental monitoring networks, and wearable health tracking devices.
Students study the integration of AI and robotics, focusing on autonomous navigation, perception systems, and human-robot interaction. The course includes hands-on experience with robotic platforms such as ROS (Robot Operating System) and simulation environments like Gazebo.
This advanced elective combines mathematical modeling with financial market analysis. Students learn to build quantitative trading strategies using Python, backtest algorithms on historical data, and evaluate risk metrics in real-world financial scenarios.
Designed for students interested in pursuing research in cybersecurity, this course covers advanced topics such as penetration testing, cryptography, malware analysis, and incident response. Students engage in ethical hacking labs and contribute to security-related projects within the department.
This elective explores immersive technologies through practical development of AR/VR applications using Unity, Unreal Engine, and specialized hardware like Oculus Rift or HTC Vive. Students learn about spatial computing, interaction design, and user experience in virtual environments.
Focused on the design and evaluation of interactive systems, this course integrates cognitive psychology, usability testing, and prototyping techniques. Students develop interfaces that are intuitive, accessible, and aligned with user needs across various domains including education, healthcare, and entertainment.
This course provides a comprehensive guide to developing mobile applications for iOS and Android platforms. Students learn about UI/UX design principles, cross-platform development using Flutter or React Native, and deployment strategies on app stores.
Students explore advanced concepts in database design, including NoSQL databases, distributed systems, indexing techniques, and query optimization. The course also covers data warehousing, ETL processes, and integration with big data tools for enterprise-level applications.
The department strongly believes that project-based learning is essential for developing practical skills and preparing students for real-world challenges. Our approach emphasizes collaborative work, iterative design, and continuous feedback throughout the project lifecycle.
Mini-projects are assigned during the second year of study and involve teams of 3-5 students working on a specific problem or technology within the scope of Computer Applications. Each project has clear learning objectives, defined deliverables, and milestones that align with industry standards.
The final-year thesis represents the culmination of the student’s academic journey. Students select a topic in consultation with faculty advisors, conduct research or develop an innovative solution, and present their findings to a panel of experts. The project must demonstrate originality, technical depth, and practical relevance.
Each student is paired with a faculty mentor who guides them through the project process, provides feedback on progress, and ensures alignment with academic rigor and industry relevance. Mentors are selected based on their expertise and availability, ensuring personalized attention for each student.
Projects are evaluated based on multiple criteria including technical execution, innovation, presentation quality, teamwork, and adherence to timelines. A rubric is used to ensure consistent grading across all projects, promoting fairness and transparency in assessment.
