Comprehensive Course Structure
The Computer Science curriculum at Plaksha University Mohali is meticulously structured across eight semesters to ensure a progressive and holistic learning experience. The program includes core courses, departmental electives, science electives, and laboratory components that are designed to build both theoretical understanding and practical application.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CSE101 | Introduction to Programming with Python | 3-0-0-3 | - |
| 1 | CSE102 | Mathematics for Computer Science I | 4-0-0-4 | - |
| 1 | CSE103 | Engineering Graphics and Design | 2-0-0-2 | - |
| 1 | SC101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | SC102 | Chemistry Laboratory | 0-0-2-1 | - |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | CSE202 | Mathematics for Computer Science II | 4-0-0-4 | CSE102 |
| 2 | CSE203 | Digital Logic and Computer Organization | 3-0-0-3 | - |
| 2 | SC201 | Biology for Engineers | 3-0-0-3 | - |
| 2 | SC202 | Mathematics Lab I | 0-0-2-1 | - |
| 3 | CSE301 | Database Management Systems | 3-0-0-3 | CSE201 |
| 3 | CSE302 | Operating Systems | 3-0-0-3 | CSE203 |
| 3 | CSE303 | Computer Networks | 3-0-0-3 | CSE201 |
| 3 | DE301 | Introduction to Software Engineering | 3-0-0-3 | - |
| 3 | DE302 | Human Computer Interaction | 3-0-0-3 | - |
| 4 | CSE401 | Machine Learning | 3-0-0-3 | CSE201 |
| 4 | CSE402 | Computer Vision | 3-0-0-3 | CSE201 |
| 4 | CSE403 | Distributed Systems | 3-0-0-3 | CSE203 |
| 4 | DE401 | Advanced Software Engineering | 3-0-0-3 | DE301 |
| 4 | DE402 | Cybersecurity Fundamentals | 3-0-0-3 | - |
| 5 | CSE501 | Deep Learning | 3-0-0-3 | CSE401 |
| 5 | CSE502 | Natural Language Processing | 3-0-0-3 | CSE401 |
| 5 | DE501 | Cloud Computing | 3-0-0-3 | - |
| 5 | DE502 | Big Data Analytics | 3-0-0-3 | - |
| 6 | CSE601 | Reinforcement Learning | 3-0-0-3 | CSE401 |
| 6 | CSE602 | Internet of Things | 3-0-0-3 | - |
| 6 | DE601 | Blockchain Technologies | 3-0-0-3 | - |
| 7 | CSE701 | Research Methodology | 2-0-0-2 | - |
| 7 | DE701 | Capstone Project I | 3-0-0-3 | - |
| 8 | DE801 | Capstone Project II | 4-0-0-4 | DE701 |
Advanced Departmental Electives
Departmental electives play a crucial role in allowing students to explore areas of personal interest and professional relevance. These courses are designed to provide deep insights into specialized domains such as AI, cybersecurity, cloud computing, and data science.
- Machine Learning: This course delves into supervised and unsupervised learning algorithms, neural networks, and reinforcement learning techniques, preparing students for roles in data science and artificial intelligence.
- Computer Vision: Students learn how to process and interpret visual information using deep learning models, with applications in robotics, medical imaging, and autonomous vehicles.
- Distributed Systems: This course focuses on the design and implementation of systems that span multiple computers, covering topics like consensus algorithms, fault tolerance, and scalability principles.
- Advanced Software Engineering: Emphasizes modern software development practices including agile methodologies, DevOps, and system design patterns.
- Cybersecurity Fundamentals: Covers essential concepts in information security, including encryption, network security, and ethical hacking.
- Cloud Computing: Introduces cloud platforms like AWS, Azure, and GCP, focusing on deployment strategies, virtualization, and scalable architecture design.
- Big Data Analytics: Explores tools such as Hadoop, Spark, and NoSQL databases to analyze large datasets for business intelligence and predictive modeling.
- Reinforcement Learning: Focuses on decision-making in uncertain environments using algorithms that learn optimal actions through trial and error.
- Internet of Things (IoT): Covers sensor networks, embedded systems, and wireless communication protocols used in smart cities and industrial automation.
- Blockchain Technologies: Provides an overview of blockchain architecture, smart contracts, and decentralized applications with real-world use cases.
Project-Based Learning Philosophy
At Plaksha University Mohali, project-based learning is the cornerstone of our academic approach. Students engage in both mini-projects and a final-year thesis that reflect their interests and career aspirations. The program emphasizes hands-on experimentation, collaboration, and innovation.
Mini-projects are introduced in the second year and require students to work in teams on real-world problems assigned by faculty or industry partners. These projects are evaluated based on technical execution, teamwork, and presentation skills.
The final-year capstone project is a significant component of the program, where students select an area of interest and work under the guidance of a faculty mentor. Projects may lead to publications, patents, or startup ventures, offering students tangible outcomes that enhance their professional profiles.