Comprehensive Course Breakdown
The Bachelor of Computer Science program at Prashanti Institute of Technology and Science is meticulously structured across eight semesters, combining core subjects, departmental electives, science electives, and practical laboratory components. The following table outlines the detailed structure:
| 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 Computing | 3-0-0-3 | - |
| 1 | CS104 | English Communication Skills | 2-0-0-2 | - |
| 1 | CS105 | Computer Organization | 3-0-0-3 | - |
| 1 | CS106 | Basic Electrical Engineering | 3-0-0-3 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Mathematics II | 3-0-0-3 | CS102 |
| 2 | CS203 | Digital Logic and Design | 3-0-0-3 | CS105 |
| 2 | CS204 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS205 | Database Management Systems | 3-0-0-3 | CS201 |
| 2 | CS206 | Discrete Mathematics | 3-0-0-3 | CS102 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS201 |
| 3 | CS303 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS304 | Web Technologies | 3-0-0-3 | CS204 |
| 3 | CS305 | Mathematics III | 3-0-0-3 | CS202 |
| 3 | CS306 | Signals and Systems | 3-0-0-3 | CS103 |
| 4 | CS401 | Design and Analysis of Algorithms | 3-0-0-3 | CS201 |
| 4 | CS402 | Compiler Design | 3-0-0-3 | CS301 |
| 4 | CS403 | Artificial Intelligence | 3-0-0-3 | CS201 |
| 4 | CS404 | Cryptography and Network Security | 3-0-0-3 | CS205 |
| 4 | CS405 | Data Mining | 3-0-0-3 | CS201 |
| 4 | CS406 | Machine Learning | 3-0-0-3 | CS401 |
| 5 | CS501 | Mobile Application Development | 3-0-0-3 | CS204 |
| 5 | CS502 | Embedded Systems | 3-0-0-3 | CS201 |
| 5 | CS503 | Cloud Computing | 3-0-0-3 | CS301 |
| 5 | CS504 | User Experience Design | 3-0-0-3 | CS204 |
| 5 | CS505 | Big Data Analytics | 3-0-0-3 | CS201 |
| 5 | CS506 | Quantitative Finance | 3-0-0-3 | CS202 |
| 6 | CS601 | Reinforcement Learning | 3-0-0-3 | CS406 |
| 6 | CS602 | Internet of Things | 3-0-0-3 | CS502 |
| 6 | CS603 | DevOps and CI/CD | 3-0-0-3 | CS301 |
| 6 | CS604 | Computer Vision | 3-0-0-3 | CS406 |
| 6 | CS605 | Financial Modeling | 3-0-0-3 | CS506 |
| 6 | CS606 | Natural Language Processing | 3-0-0-3 | CS406 |
| 7 | CS701 | Capstone Project I | 3-0-0-3 | CS501, CS502, CS503 |
| 7 | CS702 | Research Methodology | 3-0-0-3 | - |
| 7 | CS703 | Project Proposal Writing | 2-0-0-2 | - |
| 7 | CS704 | Technical Communication | 2-0-0-2 | - |
| 7 | CS705 | Industry Internship | 0-0-0-6 | - |
| 8 | CS801 | Capstone Project II | 3-0-0-3 | CS701 |
| 8 | CS802 | Final Thesis | 3-0-0-3 | CS701 |
| 8 | CS803 | Entrepreneurship and Innovation | 2-0-0-2 | - |
| 8 | CS804 | Professional Ethics | 2-0-0-2 | - |
| 8 | CS805 | Career Guidance and Interview Preparation | 2-0-0-2 | - |
Advanced Departmental Electives
Departmental electives are designed to deepen students' understanding of specialized areas within Computer Science. These courses are offered in the latter semesters and are typically taken by students who wish to explore advanced topics relevant to their chosen specialization.
Reinforcement Learning
This course focuses on algorithms used in machine learning environments where agents learn through trial-and-error interactions with an environment. Students will study Markov Decision Processes, Q-Learning, Policy Gradients, and Actor-Critic methods. Real-world applications include autonomous vehicles, robotics control, and game-playing AI systems.
Internet of Things
Students are introduced to concepts of sensor networks, wireless communication protocols, embedded system design, and data processing techniques for IoT applications. Practical components involve building prototypes using Raspberry Pi, Arduino boards, and cloud platforms like AWS IoT Core or Google Cloud IoT.
DevOps and CI/CD
This elective explores modern development practices including continuous integration, deployment automation, containerization with Docker, orchestration with Kubernetes, and infrastructure-as-code using tools like Terraform. Students gain hands-on experience through lab sessions and real-world project simulations.
Computer Vision
Students learn fundamental image processing techniques, feature extraction, object detection, classification, and deep learning models for visual recognition tasks. The course includes practical implementation of CNN architectures, OpenCV libraries, and computer vision APIs from TensorFlow and PyTorch.
Financial Modeling
This course combines financial theory with computational methods to model market behavior, assess risk, and optimize investment strategies. Topics include derivatives pricing, portfolio optimization, algorithmic trading strategies, and quantitative analysis of financial data using Python and R.
Natural Language Processing
Students study text processing, linguistic parsing, sentiment analysis, language generation, and neural language models. Practical projects involve building chatbots, translating text between languages, and developing applications for speech recognition or summarization services.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means of bridging theory with practice. Projects are designed to simulate real-world scenarios, encouraging students to collaborate effectively, think critically, and solve complex problems using multiple disciplines.
Mini-Projects
Throughout the program, students engage in mini-projects that span 1-2 months. These projects allow exploration of specific domains under faculty supervision and often culminate in presentations or peer reviews. Mini-projects may focus on areas such as web scraping, data visualization, algorithmic puzzles, or system architecture designs.
Final-Year Thesis/Capstone Project
The capstone project is the culmination of a student's academic journey. It spans two semesters and involves extensive research, experimentation, documentation, and presentation. Students work closely with faculty mentors to select projects aligned with their interests and career goals. Projects are often submitted for publication or patent filing, and many result in startups or internships at top tech firms.
Project Selection Process
Students begin exploring potential thesis topics in the seventh semester, guided by their academic advisor and faculty mentors. The selection process involves identifying research gaps, reviewing literature, and proposing innovative solutions. Faculty members provide feedback on feasibility, scope, and novelty before final approval.