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Fees
₹2,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Fees
₹2,50,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Seats
150
Students
300
Seats
150
Students
300
The Computer Applications program at Dr D Y Patil Dnyan Prasad Pune is structured over eight semesters, with a carefully designed curriculum that progresses from foundational knowledge to specialized expertise. Each semester includes core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Mathematics for Computer Science | 4-0-0-4 | - |
| I | CS103 | Computer Organization and Architecture | 3-0-0-3 | - |
| I | CS104 | Introduction to Data Structures | 3-0-0-3 | CS101 |
| I | SC101 | Physics for Engineering | 3-0-0-3 | - |
| I | SC102 | Chemistry for Engineers | 3-0-0-3 | - |
| II | CS201 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| II | CS202 | Discrete Mathematics | 4-0-0-4 | CS102 |
| II | CS203 | Database Management Systems | 3-0-0-3 | CS104 |
| II | CS204 | Algorithm Design and Analysis | 3-0-0-3 | CS104 |
| II | SC201 | Engineering Mathematics II | 4-0-0-4 | CS102 |
| III | CS301 | Data Structures and Algorithms | 3-0-0-3 | CS204 |
| III | CS302 | Operating Systems | 3-0-0-3 | CS201 |
| III | CS303 | Software Engineering | 3-0-0-3 | CS201 |
| III | CS304 | Computer Networks | 3-0-0-3 | CS203 |
| III | SC301 | Probability and Statistics | 3-0-0-3 | CS102 |
| IV | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| IV | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS304 |
| IV | CS403 | Web Technologies | 3-0-0-3 | CS201 |
| IV | CS404 | Data Science and Analytics | 3-0-0-3 | SC301 |
| IV | SC401 | Signals and Systems | 3-0-0-3 | SC201 |
| V | CS501 | Advanced Data Structures | 3-0-0-3 | CS301 |
| V | CS502 | Distributed Systems | 3-0-0-3 | CS302 |
| V | CS503 | Cloud Computing | 3-0-0-3 | CS304 |
| V | CS504 | Human Computer Interaction | 3-0-0-3 | CS303 |
| V | SC501 | Optimization Techniques | 3-0-0-3 | SC301 |
| VI | CS601 | Deep Learning | 3-0-0-3 | CS401 |
| VI | CS602 | Network Security | 3-0-0-3 | CS402 |
| VI | CS603 | Mobile Application Development | 3-0-0-3 | CS303 |
| VI | CS604 | Big Data Technologies | 3-0-0-3 | CS404 |
| VI | SC601 | Control Systems | 3-0-0-3 | SC401 |
| VII | CS701 | Capstone Project | 2-0-6-4 | All previous courses |
| VIII | CS801 | Research Thesis | 2-0-6-4 | All previous courses |
Advanced departmental electives play a crucial role in shaping students' expertise and research capabilities. Here are detailed descriptions of some advanced courses:
This course introduces students to the fundamental concepts of machine learning, including supervised and unsupervised learning algorithms. Students learn to implement these techniques using Python libraries like scikit-learn and TensorFlow.
This course covers essential cybersecurity principles such as encryption, network security, and digital forensics. It includes practical labs where students simulate real-world cyber attacks and defense mechanisms.
This course explores modern web development technologies including HTML5, CSS3, JavaScript frameworks like React and Angular, and backend development with Node.js and databases.
This course focuses on statistical methods for data analysis, including regression, classification, clustering, and time series forecasting. Students use tools like R and Python to analyze datasets and extract insights.
This course builds upon foundational knowledge of data structures by exploring complex data structures such as trees, graphs, and hash tables. It emphasizes algorithmic complexity and implementation strategies.
This course delves into the principles of distributed computing, covering topics like consensus algorithms, fault tolerance, and cloud computing architectures. Students gain hands-on experience with distributed systems frameworks.
This course provides a comprehensive overview of cloud computing technologies including virtualization, containerization, and microservices architecture. It includes lab sessions on platforms like AWS, Azure, and Google Cloud.
This course explores the design and evaluation of user interfaces. Students learn about usability principles, prototyping techniques, and user experience research methods through practical projects.
This advanced course covers deep learning architectures including convolutional neural networks, recurrent neural networks, and transformers. It includes hands-on implementation using PyTorch and TensorFlow.
This course examines advanced network security topics such as firewalls, intrusion detection systems, and secure communication protocols. Students conduct penetration testing and vulnerability assessments in controlled environments.
This course teaches students to develop cross-platform mobile applications using frameworks like Flutter and React Native. It includes building apps for both iOS and Android platforms.
This course introduces big data processing technologies including Hadoop, Spark, and NoSQL databases. Students learn to process large-scale datasets and extract meaningful information using these tools.
Our department strongly believes in project-based learning as a means of enhancing practical skills and deepening conceptual understanding. Projects are integrated throughout the curriculum, starting from first-year mini-projects to final-year capstone projects.
Mini-projects are undertaken in small groups during early semesters to reinforce concepts learned in theory classes. These projects typically last 2-3 weeks and involve designing, implementing, and presenting a solution to a specific problem.
The final-year thesis/capstone project is a significant undertaking that spans the entire semester. Students select a topic aligned with their interests or industry needs, work closely with faculty mentors, and produce a substantial deliverable including documentation, code, and a presentation.
Project selection involves an interactive process where students present ideas to faculty advisors who help refine them into feasible research questions. Faculty members guide students through the entire lifecycle of their projects, from planning to execution to final review.
