Comprehensive Course Structure
The Computer Science curriculum at P K University Shivpuri is meticulously structured over 8 semesters to ensure a seamless progression from foundational knowledge to advanced specialization. The program includes core courses, departmental electives, science electives, and practical labs designed to foster both theoretical understanding and real-world application.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics I | 4-0-0-4 | - |
| 1 | CS103 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CS104 | English for Technical Communication | 2-0-0-2 | - |
| 1 | CS105 | Computer Fundamentals | 2-0-0-2 | - |
| 1 | CS106 | Workshop on Hardware and Software | 0-0-4-2 | - |
| 2 | CS201 | Data Structures & Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Mathematics II | 4-0-0-4 | CS102 |
| 2 | CS203 | Electrical Circuits & Devices | 3-0-0-3 | - |
| 2 | CS204 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS205 | Computer Organization | 3-0-0-3 | CS105 |
| 2 | CS206 | Lab: Programming & Problem Solving | 0-0-4-2 | CS101 |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Theory of Computation | 3-0-0-3 | CS201 |
| 3 | CS303 | Operating Systems | 3-0-0-3 | CS205 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS305 | Mathematics III | 4-0-0-4 | CS202 |
| 3 | CS306 | Lab: Database & OS | 0-0-4-2 | CS201, CS205 |
| 4 | CS401 | Computer Networks | 3-0-0-3 | CS303 |
| 4 | CS402 | Distributed Systems | 3-0-0-3 | CS303 |
| 4 | CS403 | Artificial Intelligence | 3-0-0-3 | CS301, CS302 |
| 4 | CS404 | Cybersecurity Fundamentals | 3-0-0-3 | CS301 |
| 4 | CS405 | Mathematics IV | 4-0-0-4 | CS305 |
| 4 | CS406 | Lab: AI & Security | 0-0-4-2 | CS301, CS304 |
| 5 | CS501 | Machine Learning | 3-0-0-3 | CS403 |
| 5 | CS502 | Data Science & Analytics | 3-0-0-3 | CS301, CS405 |
| 5 | CS503 | Web Technologies | 3-0-0-3 | CS204 |
| 5 | CS504 | Human Computer Interaction | 3-0-0-3 | CS204 |
| 5 | CS505 | Cloud Computing | 3-0-0-3 | CS401 |
| 5 | CS506 | Lab: Web & ML | 0-0-4-2 | CS403, CS503 |
| 6 | CS601 | Big Data Analytics | 3-0-0-3 | CS502 |
| 6 | CS602 | Embedded Systems | 3-0-0-3 | CS205 |
| 6 | CS603 | Software Testing & Quality Assurance | 3-0-0-3 | CS304 |
| 6 | CS604 | Internet of Things | 3-0-0-3 | CS503 |
| 6 | CS605 | Advanced Computer Architecture | 3-0-0-3 | CS205 |
| 6 | CS606 | Lab: IoT & Embedded Systems | 0-0-4-2 | CS602, CS604 |
| 7 | CS701 | Research Methodology | 3-0-0-3 | - |
| 7 | CS702 | Advanced Topics in CS | 3-0-0-3 | CS501 |
| 7 | CS703 | Capstone Project I | 0-0-8-4 | CS601, CS602 |
| 7 | CS704 | Special Elective I | 3-0-0-3 | - |
| 7 | CS705 | Internship | 0-0-0-6 | - |
| 7 | CS706 | Lab: Capstone Project | 0-0-4-2 | CS703 |
| 8 | CS801 | Capstone Project II | 0-0-8-6 | CS703 |
| 8 | CS802 | Special Elective II | 3-0-0-3 | - |
| 8 | CS803 | Special Elective III | 3-0-0-3 | - |
| 8 | CS804 | Research Thesis | 0-0-12-6 | CS701 |
| 8 | CS805 | Professional Ethics | 2-0-0-2 | - |
| 8 | CS806 | Lab: Research Thesis | 0-0-4-2 | CS804 |
Advanced Departmental Elective Courses
The department offers a wide range of advanced elective courses designed to deepen students' understanding and enhance their specialization skills. Here are some key electives:
Machine Learning (CS501)
This course introduces students to modern machine learning techniques including supervised, unsupervised, and reinforcement learning algorithms. Students learn how to implement these models using Python libraries like Scikit-Learn, TensorFlow, and PyTorch. The curriculum includes topics such as decision trees, clustering, neural networks, natural language processing, and computer vision. Through hands-on projects, students gain practical experience in building intelligent systems that can adapt and improve over time.
Data Science & Analytics (CS502)
This course provides an in-depth exploration of data analysis methods and tools used in business intelligence and scientific research. Students learn statistical modeling, data visualization techniques, and big data processing using technologies like Hadoop, Spark, and SQL. The course emphasizes real-world applications such as predictive analytics, customer segmentation, and fraud detection. Practical assignments involve working with large datasets to extract meaningful insights and support strategic decision-making.
Web Technologies (CS503)
This course focuses on the design and development of dynamic web applications using modern frameworks and technologies. Topics include HTML/CSS/JavaScript, React.js, Node.js, RESTful APIs, and database integration with MongoDB or PostgreSQL. Students build full-stack web apps from scratch, gaining experience in responsive design, authentication, and deployment strategies. The course also covers emerging trends such as progressive web apps (PWAs), serverless computing, and GraphQL.
Human Computer Interaction (CS504)
This elective explores how users interact with digital systems and how interfaces can be designed to be more intuitive and accessible. Students study cognitive psychology, user experience (UX) principles, usability testing, and prototyping techniques. The course includes practical sessions in design tools like Figma, Adobe XD, and Sketch, enabling students to create user-centered designs for websites, mobile apps, and software products.
Cloud Computing (CS505)
This course delves into cloud computing models, architectures, and services offered by major providers such as AWS, Microsoft Azure, and Google Cloud Platform. Students learn about virtualization, containerization, microservices, DevOps practices, and scalable application deployment. Through lab exercises, students deploy applications on cloud platforms and manage resources efficiently while ensuring security and compliance.
Big Data Analytics (CS601)
This advanced course introduces students to the challenges and solutions associated with processing massive volumes of data. Students explore distributed computing frameworks like Apache Hadoop and Spark, along with NoSQL databases such as Cassandra and MongoDB. The course emphasizes real-time data streaming, batch processing, and machine learning integration for extracting actionable intelligence from big datasets.
Embedded Systems (CS602)
This elective focuses on the design and implementation of embedded systems used in automotive, healthcare, consumer electronics, and industrial applications. Students learn microcontroller programming, real-time operating systems (RTOS), hardware-software co-design, and sensor integration. The course includes lab projects involving Arduino, Raspberry Pi, and ARM Cortex-M processors to build functional embedded devices.
Software Testing & Quality Assurance (CS603)
This course equips students with the skills needed to ensure software quality throughout the development lifecycle. Topics include test case design, automation tools, performance testing, security testing, and agile methodologies. Students practice writing unit tests, integration tests, and end-to-end tests using frameworks like JUnit, Selenium, and Postman. The course also covers compliance standards such as ISO/IEC 25010.
Internet of Things (CS604)
This elective explores the architecture, protocols, and applications of IoT systems. Students study wireless communication technologies, edge computing, data privacy, and smart city initiatives. Through hands-on labs, students design and deploy IoT networks using platforms like MQTT, CoAP, and LoRaWAN, connecting sensors, actuators, and cloud services.
Advanced Computer Architecture (CS605)
This course examines the principles behind modern computer processors, memory hierarchies, and instruction set architectures. Students explore RISC-V, ARM, x86, and GPU architectures, learning how to optimize code for performance and energy efficiency. The course includes simulation exercises using tools like Gem5 and SPARK to analyze system behavior under different load conditions.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to bridge theory and practice. Every student is expected to complete several mini-projects throughout their academic journey, culminating in a capstone project or thesis in the final year.
Mini-Projects
Mini-projects are assigned in the second and third years, typically lasting 6–8 weeks. These projects allow students to apply concepts learned in class to solve real-world problems. Students work individually or in small teams under faculty supervision, developing skills in project planning, execution, documentation, and presentation. Projects often involve collaboration with industry partners, giving students exposure to professional environments and expectations.
Final-Year Thesis/Capstone Project
The final-year thesis or capstone project is a significant component of the program. Students select a research topic aligned with their interests and career goals, working closely with a faculty advisor throughout the process. The project involves literature review, experimental design, implementation, testing, and documentation. Students must present their work at departmental symposiums and may submit papers to conferences or journals.
Project Selection & Mentorship
Students are encouraged to choose projects that align with their career aspirations and personal interests. The selection process involves submitting a proposal outlining the problem statement, objectives, methodology, and expected outcomes. Faculty mentors are assigned based on expertise in the relevant domain, ensuring quality guidance and support. Regular progress meetings and milestone reviews help maintain momentum and address challenges.