Comprehensive Course Schedule for 8 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming with Python | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Science | 3-0-0-3 | - |
| 1 | CS103 | Engineering Graphics and Design | 2-0-0-2 | - |
| 1 | CS104 | Physics for Computer Science | 3-0-0-3 | - |
| 1 | CS105 | Chemistry and Biology | 3-0-0-3 | - |
| 1 | CS106 | English Communication Skills | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Digital Logic Design | 3-0-0-3 | - |
| 2 | CS203 | Object-Oriented Programming with C++ | 3-0-0-3 | CS101 |
| 2 | CS204 | Calculus and Linear Algebra | 3-0-0-3 | - |
| 2 | CS205 | Statistics and Probability | 3-0-0-3 | - |
| 2 | CS206 | Communication Skills Lab | 0-0-3-1 | - |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS201, CS203 |
| 3 | CS302 | Computer Architecture and Organization | 3-0-0-3 | CS202 |
| 3 | CS303 | Operating Systems | 3-0-0-3 | CS201, CS203 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS201, CS203 |
| 3 | CS305 | Discrete Mathematical Structures | 3-0-0-3 | CS204 |
| 3 | CS306 | Web Technologies Lab | 0-0-3-1 | CS203 |
| 4 | CS401 | Computer Networks | 3-0-0-3 | CS302, CS303 |
| 4 | CS402 | Artificial Intelligence and Machine Learning | 3-0-0-3 | CS201, CS305 |
| 4 | CS403 | Compiler Design | 3-0-0-3 | CS301, CS303 |
| 4 | CS404 | Human-Computer Interaction | 3-0-0-3 | CS201, CS304 |
| 4 | CS405 | Data Mining and Analytics | 3-0-0-3 | CS301, CS305 |
| 4 | CS406 | Cybersecurity Lab | 0-0-3-1 | CS301, CS401 |
| 5 | CS501 | Cloud Computing | 3-0-0-3 | CS401, CS402 |
| 5 | CS502 | Advanced Algorithms | 3-0-0-3 | CS201, CS305 |
| 5 | CS503 | Mobile Application Development | 3-0-0-3 | CS203, CS404 |
| 5 | CS504 | Software Testing and Quality Assurance | 3-0-0-3 | CS304 |
| 5 | CS505 | Big Data Technologies | 3-0-0-3 | CS301, CS405 |
| 5 | CS506 | Embedded Systems Lab | 0-0-3-1 | CS202, CS302 |
| 6 | CS601 | Research Methodology | 2-0-0-2 | - |
| 6 | CS602 | Capstone Project I | 2-0-0-2 | CS501, CS503 |
| 6 | CS603 | Special Topics in Computer Science | 3-0-0-3 | - |
| 6 | CS604 | Industrial Internship | 0-0-0-12 | - |
| 6 | CS605 | Elective I | 3-0-0-3 | - |
| 6 | CS606 | Elective II | 3-0-0-3 | - |
| 7 | CS701 | Capstone Project II | 2-0-0-2 | CS602 |
| 7 | CS702 | Advanced Elective I | 3-0-0-3 | - |
| 7 | CS703 | Advanced Elective II | 3-0-0-3 | - |
| 7 | CS704 | Professional Ethics and Social Responsibility | 2-0-0-2 | - |
| 7 | CS705 | Entrepreneurship and Innovation | 2-0-0-2 | - |
| 7 | CS706 | Final Thesis/Project | 0-0-0-15 | CS701 |
| 8 | CS801 | Advanced Research Project | 0-0-0-12 | CS706 |
| 8 | CS802 | Internship | 0-0-0-12 | - |
| 8 | CS803 | Final Presentation and Viva Voce | 0-0-0-3 | CS706 |
| 8 | CS804 | Capstone Integration | 0-0-0-3 | - |
| 8 | CS805 | Industry Exposure Workshop | 2-0-0-2 | - |
| 8 | CS806 | Capstone Portfolio Development | 0-0-0-3 | CS706 |
Advanced Departmental Elective Courses
Advanced departmental electives at Phonics Group Of Institutions offer specialized knowledge in emerging fields of computer science. These courses are designed to challenge students and prepare them for leadership roles in their chosen domains.
'Reinforcement Learning' explores algorithms that enable machines to learn from interactions with environments, focusing on applications in robotics, game theory, and autonomous systems. Students engage in practical projects using tools like TensorFlow and PyTorch to build intelligent agents capable of decision-making under uncertainty.
'Computer Vision' delves into image processing techniques, object recognition, and deep learning models for visual analysis. This course includes hands-on lab sessions where students implement neural networks for tasks such as facial recognition, medical imaging, and autonomous navigation systems.
'Natural Language Processing (NLP)' covers text mining, sentiment analysis, and language generation using transformer architectures. Students work on projects involving chatbots, machine translation, and content summarization, leveraging large language models like BERT and GPT-3.
'Cybersecurity Research' focuses on advanced topics in network security, ethical hacking, and incident response. Through simulated attacks and defensive strategies, students develop skills in threat modeling, forensic analysis, and secure system design.
'Software Architecture and Design Patterns' teaches principles of scalable software design, including microservices, cloud-native applications, and architectural patterns such as MVC and MVP. Students apply these concepts to real-world case studies and build modular applications using modern frameworks.
'Big Data Technologies' introduces students to distributed computing platforms like Apache Hadoop and Spark, enabling them to process and analyze large datasets efficiently. Practical labs involve building data pipelines and performing analytics on streaming data sources.
'Internet of Things (IoT) Security' addresses vulnerabilities in connected devices and explores secure protocols for communication between IoT systems. Students design and deploy security solutions for smart home environments, industrial sensors, and wearable technology.
'Human-Computer Interaction (HCI)' emphasizes user-centered design principles, usability testing, and accessibility standards. Through iterative design processes, students create interfaces that are intuitive, inclusive, and responsive to diverse user needs.
'Data Visualization and Storytelling' combines statistical methods with visual representation techniques to communicate complex data insights effectively. Students learn to use libraries like D3.js and Tableau to build interactive dashboards and visual narratives.
'Mobile App Development for Enterprise' focuses on developing scalable mobile applications tailored for business environments. Topics include cross-platform development, API integration, and enterprise security protocols, with students building apps for industries such as healthcare, finance, and logistics.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in experiential education that bridges the gap between theory and practice. We believe that active engagement through meaningful projects enhances critical thinking, problem-solving skills, and professional readiness among students.
Mini-projects begin in the second semester, allowing students to apply foundational concepts learned in lectures to real-world problems. These projects are typically completed within 4-6 weeks and involve small teams of 3-5 members. The evaluation criteria include technical execution, teamwork, presentation skills, and adherence to deadlines.
The final-year thesis or capstone project represents the culmination of a student's academic journey. Students select projects that align with their interests and career goals, often collaborating with faculty advisors or industry partners. The scope of these projects is determined through consultation with mentors and includes literature review, experimental design, implementation, documentation, and presentation.
Project selection involves a formal proposal process where students submit a detailed plan outlining objectives, methodology, timeline, and expected outcomes. Faculty members evaluate proposals based on originality, feasibility, relevance to current trends, and alignment with departmental research areas.
Evaluation for both mini-projects and capstone projects follows a multi-tiered approach involving peer reviews, faculty assessments, and external evaluations where applicable. Students are encouraged to present their work at conferences or publish findings in journals, further enhancing their academic profile and professional development.