Curriculum
The curriculum for Computer Applications at People S University Bhopal is meticulously designed to provide students with a balanced mix of theoretical knowledge and practical experience. It covers core disciplines such as data structures, algorithms, databases, software engineering, and computer networks, while also offering specialized tracks in emerging fields like artificial intelligence, cybersecurity, and cloud computing.
Course Listing
The following table lists all courses offered across the 8 semesters:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-0-3 | - |
| I | CS102 | Engineering Mathematics I | 3-0-0-3 | - |
| I | CS103 | Basic Electrical Engineering | 3-0-0-3 | - |
| I | CS104 | Introduction to Computer Science | 2-0-0-2 | - |
| I | CS105 | English for Technical Communication | 2-0-0-2 | - |
| I | CS106 | Physics Laboratory | 0-0-3-1 | - |
| II | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| II | CS202 | Engineering Mathematics II | 3-0-0-3 | CS102 |
| II | CS203 | Digital Logic and Computer Organization | 3-0-0-3 | CS103 |
| II | CS204 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| II | CS205 | Chemistry Laboratory | 0-0-3-1 | - |
| III | CS301 | Database Management Systems | 3-0-0-3 | CS201, CS204 |
| III | CS302 | Operating Systems | 3-0-0-3 | CS203, CS204 |
| III | CS303 | Computer Networks | 3-0-0-3 | CS203 |
| III | CS304 | Software Engineering | 3-0-0-3 | CS204 |
| IV | CS401 | Artificial Intelligence | 3-0-0-3 | CS301, CS302 |
| IV | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS303 |
| IV | CS403 | Data Science and Analytics | 3-0-0-3 | CS202, CS301 |
| IV | CS404 | Cloud Computing | 3-0-0-3 | CS303 |
| V | CS501 | Machine Learning | 3-0-0-3 | CS401, CS403 |
| V | CS502 | Blockchain Technology | 3-0-0-3 | CS402 |
| V | CS503 | Human-Computer Interaction | 3-0-0-3 | CS404 |
| V | CS504 | Mobile Application Development | 3-0-0-3 | CS304 |
| VI | CS601 | Advanced Data Structures and Algorithms | 3-0-0-3 | CS201 |
| VI | CS602 | Internet of Things (IoT) | 3-0-0-3 | CS303 |
| VI | CS603 | Quantitative Finance and Risk Modeling | 3-0-0-3 | CS403 |
| VI | CS604 | Project Management and Leadership | 3-0-0-3 | CS304 |
| VII | CS701 | Research Methodology and Ethics | 2-0-0-2 | - |
| VII | CS702 | Capstone Project | 0-0-6-3 | All previous courses |
| VIII | CS801 | Industry Internship | 0-0-6-3 | CS702 |
| VIII | CS802 | Entrepreneurship and Innovation | 2-0-0-2 | - |
Advanced Departmental Electives
The department offers several advanced elective courses that allow students to specialize in specific areas of interest:
- Deep Learning with TensorFlow: This course focuses on building neural networks using TensorFlow, covering convolutional and recurrent architectures. Students learn to implement image recognition, natural language processing, and time-series forecasting models.
- Cryptography and Network Security: An in-depth exploration of cryptographic algorithms, secure communication protocols, and intrusion detection systems. Students gain hands-on experience with tools like Wireshark and OpenSSL.
- Big Data Analytics using Hadoop: This course introduces students to big data technologies such as Hadoop, Spark, and Hive. Through practical exercises, students learn to process large datasets and derive meaningful insights.
- DevOps and CI/CD Pipelines: Covers continuous integration, deployment automation, containerization with Docker, and orchestration using Kubernetes. Students build pipelines for software delivery and maintenance.
- Computer Vision and Image Processing: Explores image segmentation, feature extraction, object detection, and facial recognition techniques using libraries like OpenCV and TensorFlow.
- Quantitative Risk Modeling: Teaches students how to model financial risks using statistical methods and Monte Carlo simulations. Real-world case studies from global banks are used to illustrate concepts.
- Game Development with Unity: Introduces game development using the Unity engine, covering scripting, physics, UI design, and asset management.
- Neural Network Architectures: Focuses on advanced architectures such as Transformers, GANs, and attention mechanisms. Students implement these models for NLP and computer vision tasks.
- Mobile App Security: Examines vulnerabilities in mobile applications and defensive strategies. Students perform penetration testing on apps and learn secure coding practices.
- Quantum Computing Fundamentals: Introduces quantum algorithms, qubits, superposition, and entanglement. Students simulate quantum circuits using tools like Qiskit and Cirq.
Project-Based Learning Philosophy
The department places great emphasis on project-based learning as a means to enhance student engagement and practical understanding. Mini-projects are introduced from the second year, allowing students to apply theoretical knowledge in real-world scenarios.
Mini-projects typically last 4-6 weeks and are evaluated based on technical execution, documentation quality, team collaboration, and presentation skills. These projects are assigned based on student interest, faculty expertise, and alignment with industry trends.
The final-year thesis/capstone project is a major undertaking that spans the entire academic year. Students select a topic in consultation with faculty mentors and work independently or in teams to develop a substantial piece of research or application.
Evaluation criteria include:
- Creativity and innovation
- Technical depth and complexity
- Documentation quality and clarity
- Presentation and communication skills
- Impact and relevance to industry needs
Students can choose their projects from a list of suggested topics provided by faculty or propose their own ideas. Faculty mentors are assigned based on expertise in the chosen area, ensuring that students receive guidance tailored to their specific interests.