Comprehensive Course Structure
The Computer Science program at Nayanta University Pune spans four years with a total of eight semesters. Each semester includes core courses, departmental electives, science electives, and laboratory components designed to build a comprehensive understanding of the field.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics I | 3-0-0-3 | - |
| 1 | CS103 | Physics for Computer Science | 3-0-0-3 | - |
| 1 | CS104 | English for Technical Communication | 2-0-0-2 | - |
| 1 | CS105 | Introduction to Computer Science | 2-0-0-2 | - |
| 1 | CS106 | Lab: Programming Fundamentals | 0-0-3-1 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Mathematics II | 3-0-0-3 | CS102 |
| 2 | CS203 | Digital Logic Design | 3-0-0-3 | - |
| 2 | CS204 | Object-Oriented Programming with Java | 2-0-0-2 | CS101 |
| 2 | CS205 | Computer Organization and Architecture | 3-0-0-3 | CS103 |
| 2 | CS206 | Lab: Data Structures & Algorithms | 0-0-3-1 | CS101, CS201 |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS205 |
| 3 | CS303 | Computer Networks | 3-0-0-3 | CS201, CS205 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS305 | Mathematics III | 3-0-0-3 | CS202 |
| 3 | CS306 | Lab: Operating Systems | 0-0-3-1 | CS205, CS302 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-0-0-3 | CS201 |
| 4 | CS402 | Artificial Intelligence | 3-0-0-3 | CS201, CS301 |
| 4 | CS403 | Cryptography and Network Security | 3-0-0-3 | CS303 |
| 4 | CS404 | Web Technologies | 2-0-0-2 | CS204 |
| 4 | CS405 | Mathematics IV | 3-0-0-3 | CS202 |
| 4 | CS406 | Lab: Web Technologies | 0-0-3-1 | CS204, CS404 |
| 5 | CS501 | Machine Learning | 3-0-0-3 | CS201, CS301 |
| 5 | CS502 | Big Data Analytics | 3-0-0-3 | CS301 |
| 5 | CS503 | Distributed Systems | 3-0-0-3 | CS303 |
| 5 | CS504 | Human-Computer Interaction | 2-0-0-2 | - |
| 5 | CS505 | Database Internals | 3-0-0-3 | CS301 |
| 5 | CS506 | Lab: Machine Learning | 0-0-3-1 | CS501 |
| 6 | CS601 | Deep Learning | 3-0-0-3 | CS501 |
| 6 | CS602 | Computer Vision | 3-0-0-3 | CS501 |
| 6 | CS603 | Security Protocols | 3-0-0-3 | CS403 |
| 6 | CS604 | Cloud Computing | 3-0-0-3 | CS303 |
| 6 | CS605 | Natural Language Processing | 3-0-0-3 | CS501 |
| 6 | CS606 | Lab: Deep Learning | 0-0-3-1 | CS601 |
| 7 | CS701 | Advanced Algorithms | 3-0-0-3 | CS401 |
| 7 | CS702 | Quantum Computing | 3-0-0-3 | - |
| 7 | CS703 | Reinforcement Learning | 3-0-0-3 | CS501 |
| 7 | CS704 | Mobile Application Development | 2-0-0-2 | CS204 |
| 7 | CS705 | Research Methodology | 2-0-0-2 | - |
| 7 | CS706 | Lab: Quantum Computing | 0-0-3-1 | CS702 |
| 8 | CS801 | Capstone Project | 3-0-0-6 | All previous semesters |
| 8 | CS802 | Internship | 0-0-0-3 | - |
| 8 | CS803 | Technical Elective I | 3-0-0-3 | - |
| 8 | CS804 | Technical Elective II | 3-0-0-3 | - |
| 8 | CS805 | Technical Elective III | 3-0-0-3 | - |
| 8 | CS806 | Lab: Capstone Project | 0-0-3-2 | CS801 |
This structured approach ensures a logical progression from foundational concepts to advanced applications. Students are encouraged to explore various domains through elective courses tailored to their interests and career aspirations.
Detailed Overview of Departmental Electives
Deep Learning (CS601): This course delves into the theory and practice of deep neural networks, covering convolutional, recurrent, and transformer architectures. Students will implement models using TensorFlow or PyTorch and apply them to image classification, natural language processing, and speech recognition tasks.
Computer Vision (CS602): Focused on techniques for analyzing visual data, this course introduces students to edge detection, object recognition, segmentation, and 3D reconstruction. Practical components involve working with datasets like ImageNet and COCO to build real-world vision systems.
Security Protocols (CS603): Designed to provide comprehensive knowledge of cryptographic algorithms, secure communication protocols, and network security mechanisms. Students will study both classical and modern encryption standards and conduct penetration testing exercises.
Cloud Computing (CS604): This elective explores cloud infrastructure, virtualization technologies, containerization tools like Docker, and orchestration platforms such as Kubernetes. It includes hands-on labs with AWS, Azure, and GCP services to deploy scalable applications.
Natural Language Processing (CS605): Students learn advanced NLP techniques including sentiment analysis, language modeling, machine translation, and question answering systems. The course utilizes libraries like spaCy, NLTK, and Hugging Face Transformers for practical implementation.
Advanced Algorithms (CS701): Building upon earlier algorithmic foundations, this course covers complexity theory, approximation algorithms, graph algorithms, and dynamic programming techniques. It prepares students for competitive programming and advanced research in computational problems.
Quantum Computing (CS702): Introduces fundamental concepts of quantum mechanics, qubits, superposition, entanglement, and quantum gates. Students will simulate quantum circuits using Qiskit and explore current applications in optimization and cryptography.
Reinforcement Learning (CS703): This course explores decision-making processes in uncertain environments through Markov Decision Processes, policy gradients, and value iteration methods. Students implement agents for games like Atari and robotics simulations.
Mobile Application Development (CS704): Covers cross-platform mobile app development using frameworks like Flutter and React Native. Emphasis is placed on UI/UX design principles, backend integration, and deployment strategies.
Research Methodology (CS705): Prepares students for research-oriented work by teaching literature review techniques, hypothesis formulation, data collection methods, and scientific writing standards. Students will conduct a small-scale research project under faculty supervision.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based education. From the first year, students are encouraged to work on mini-projects that integrate theoretical concepts with practical implementation. These projects often involve real-world challenges posed by industry partners or faculty research initiatives.
Mini-projects span two semesters and typically involve teams of 3–5 students working under the guidance of a faculty mentor. The structure includes weekly progress reports, milestone evaluations, and final presentations. Projects are assessed based on innovation, technical depth, teamwork, and documentation quality.
The final-year capstone project is a significant undertaking where students design and develop an independent solution or product addressing a relevant problem in the field of Computer Science. Students have access to dedicated research labs, mentorship from faculty members, and funding for prototype development. The project culminates in a public presentation and a detailed written report submitted to the departmental board.
Faculty mentors are selected based on expertise alignment with student interests, ensuring that each team receives specialized guidance. Regular workshops and seminars help students refine their skills and stay updated with emerging trends in technology.