Comprehensive Curriculum Overview
The Computer Science program at Presidency University Bangalore is designed to provide students with a well-rounded education that combines theoretical knowledge with practical application. The curriculum is structured across eight semesters, with each semester building upon the previous one to ensure a progressive learning experience.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | CS102 | Physics for Computer Science | 3-1-0-4 | - |
| 1 | CS103 | Introduction to Programming | 3-1-0-4 | - |
| 1 | CS104 | Computer Fundamentals | 2-0-0-2 | - |
| 1 | CS105 | English for Technical Communication | 2-0-0-2 | - |
| 2 | CS201 | Engineering Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Chemistry for Computer Science | 3-1-0-4 | - |
| 2 | CS203 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| 2 | CS204 | Object Oriented Programming | 3-1-0-4 | CS103 |
| 2 | CS205 | Computer Organization and Architecture | 3-1-0-4 | - |
| 3 | CS301 | Probability and Statistics | 3-1-0-4 | CS201 |
| 3 | CS302 | Operating Systems | 3-1-0-4 | CS204, CS205 |
| 3 | CS303 | Database Management Systems | 3-1-0-4 | CS204 |
| 3 | CS304 | Software Engineering | 3-1-0-4 | CS204 |
| 3 | CS305 | Microprocessors and Microcontrollers | 3-1-0-4 | CS205 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-1-0-4 | CS203 |
| 4 | CS402 | Computer Networks | 3-1-0-4 | CS205, CS302 |
| 4 | CS403 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS301, CS203 |
| 4 | CS404 | Cybersecurity Fundamentals | 3-1-0-4 | CS205 |
| 4 | CS405 | Data Mining and Big Data Analytics | 3-1-0-4 | CS301, CS303 |
| 5 | CS501 | Advanced Algorithms | 3-1-0-4 | CS401 |
| 5 | CS502 | Distributed Systems | 3-1-0-4 | CS402 |
| 5 | CS503 | Human Computer Interaction | 3-1-0-4 | CS304 |
| 5 | CS504 | Cloud Computing | 3-1-0-4 | CS402, CS302 |
| 5 | CS505 | Internet of Things | 3-1-0-4 | CS305 |
| 6 | CS601 | Research Methodology and Project Management | 2-0-0-2 | - |
| 6 | CS602 | Special Topics in Computer Science | 3-1-0-4 | - |
| 6 | CS603 | Capstone Project I | 2-0-0-2 | - |
| 6 | CS604 | Mini Project I | 2-0-0-2 | - |
| 7 | CS701 | Advanced Machine Learning | 3-1-0-4 | CS403 |
| 7 | CS702 | Security Architecture and Cryptography | 3-1-0-4 | CS404 |
| 7 | CS703 | Data Science and Visualization | 3-1-0-4 | CS501, CS405 |
| 7 | CS704 | Embedded Systems | 3-1-0-4 | CS305 |
| 7 | CS705 | Capstone Project II | 2-0-0-2 | - |
| 8 | CS801 | Final Year Thesis | 4-0-0-4 | - |
| 8 | CS802 | Internship | 4-0-0-4 | - |
Advanced Departmental Elective Courses
The department offers a wide range of advanced departmental elective courses that allow students to specialize in their areas of interest while maintaining the flexibility to explore diverse computing domains.
Deep Learning is one such course that provides students with an in-depth understanding of neural networks, convolutional networks, recurrent networks, and transformers. The course emphasizes practical implementation using frameworks like TensorFlow and PyTorch, preparing students for research and development roles in AI.
Natural Language Processing (NLP) explores the intersection of computational linguistics and artificial intelligence, focusing on techniques such as sentiment analysis, language modeling, and machine translation. Students work on real-world datasets to build applications that understand and generate human language.
Computer Vision is a course that delves into image processing, object detection, facial recognition, and image segmentation. Students learn to implement algorithms using libraries like OpenCV and use deep learning models for visual recognition tasks.
Reinforcement Learning introduces students to the principles of decision-making in dynamic environments. The course covers Markov Decision Processes, Q-learning, policy gradients, and actor-critic methods, preparing students for careers in autonomous systems and game AI.
Cryptography and Network Security covers both classical and modern cryptographic techniques, including symmetric and asymmetric encryption, hash functions, and digital signatures. Students learn to implement secure communication protocols and protect against various cyber threats.
Software Architecture and Design Patterns explores the principles of designing scalable and maintainable software systems. Students study patterns such as MVC, MVVM, microservices architecture, and domain-driven design, preparing them for leadership roles in software development.
Big Data Technologies introduces students to Hadoop, Spark, and other distributed computing frameworks. The course focuses on processing large datasets efficiently and building data pipelines that can handle real-time streaming data.
Mobile Application Development covers the principles of designing and developing applications for iOS and Android platforms. Students learn to build cross-platform apps using frameworks like React Native and Flutter, preparing them for mobile development careers.
Quantum Computing provides an introduction to quantum mechanics and its application in computing. The course covers quantum algorithms, quantum circuits, and simulation techniques, preparing students for the emerging field of quantum information science.
Distributed Systems and Cloud Computing explores the design and implementation of systems that operate across multiple computers. Students study concepts such as consensus protocols, load balancing, and cloud deployment strategies, preparing them for roles in system architecture and DevOps.
Project-Based Learning Philosophy
The Computer Science department at Presidency University Bangalore is committed to project-based learning as a core pedagogical approach. This methodology emphasizes the development of practical skills through real-world problem-solving experiences.
Mini Projects are introduced in the early semesters and gradually increase in complexity and scope. These projects are designed to help students apply theoretical concepts learned in class to practical scenarios, fostering creativity and innovation.
The Final Year Thesis or Capstone Project is the culmination of the undergraduate experience. Students work under the guidance of faculty mentors on a substantial research or development project that addresses real-world challenges. This project allows students to demonstrate their mastery of the field and prepares them for post-graduation opportunities in academia or industry.
Project selection is an important process where students can choose from a list of faculty-led projects or propose their own ideas. The department facilitates this by providing access to research facilities, mentorship, and funding for necessary resources.
Evaluation criteria for projects are comprehensive, considering factors such as technical competency, innovation, documentation quality, presentation skills, and teamwork. This holistic approach ensures that students develop not just technical skills but also essential soft skills required in professional environments.