Comprehensive Curriculum Structure for Computer Applications
Semester-wise Course Breakdown
| 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 | Physics for Engineers | 3-0-0-3 | - |
| I | CS104 | Chemistry for Engineering | 3-0-0-3 | - |
| I | CS105 | English Communication Skills | 2-0-0-2 | - |
| I | CS106 | Computer Fundamentals | 3-0-0-3 | - |
| 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 | - |
| II | CS204 | Database Management Systems | 3-0-0-3 | - |
| II | CS205 | Operating Systems | 3-0-0-3 | - |
| III | CS301 | Computer Networks | 3-0-0-3 | CS203 |
| III | CS302 | Object-Oriented Programming with Java | 3-0-0-3 | CS101 |
| III | CS303 | Web Technologies | 3-0-0-3 | CS204 |
| III | CS304 | Software Engineering | 3-0-0-3 | - |
| III | CS305 | Discrete Mathematical Structures | 3-0-0-3 | CS102 |
| IV | CS401 | Advanced Data Structures | 3-0-0-3 | CS201 |
| IV | CS402 | Machine Learning Fundamentals | 3-0-0-3 | CS201 |
| IV | CS403 | Database Design and Optimization | 3-0-0-3 | CS204 |
| IV | CS404 | Cybersecurity Essentials | 3-0-0-3 | - |
| IV | CS405 | Cloud Computing Concepts | 3-0-0-3 | - |
| V | CS501 | Big Data Analytics | 3-0-0-3 | CS402 |
| V | CS502 | Mobile Application Development | 3-0-0-3 | CS303 |
| V | CS503 | Internet of Things (IoT) Applications | 3-0-0-3 | - |
| V | CS504 | Human-Computer Interaction | 3-0-0-3 | - |
| V | CS505 | Artificial Intelligence and Robotics | 3-0-0-3 | CS402 |
| VI | CS601 | Software Project Management | 3-0-0-3 | CS304 |
| VI | CS602 | Distributed Systems | 3-0-0-3 | CS301 |
| VI | CS603 | Data Mining and Knowledge Discovery | 3-0-0-3 | CS501 |
| VI | CS604 | Network Security and Cryptography | 3-0-0-3 | CS404 |
| VI | CS605 | Mobile and Web Development | 3-0-0-3 | CS303 |
| VII | CS701 | Capstone Project I | 4-0-0-4 | - |
| VII | CS702 | Research Methodology | 2-0-0-2 | - |
| VII | CS703 | Professional Ethics and Legal Issues | 2-0-0-2 | - |
| VII | CS704 | Industrial Training | 3-0-0-3 | - |
| VIII | CS801 | Capstone Project II | 4-0-0-4 | CS701 |
| VIII | CS802 | Final Year Project | 6-0-0-6 | - |
| VIII | CS803 | Internship Report Writing | 2-0-0-2 | - |
Advanced Departmental Electives
These advanced courses are designed to deepen students' understanding of specialized domains within computer applications and prepare them for leadership roles in industry and academia.
1. Machine Learning and Deep Learning
This course delves into the mathematical foundations of machine learning algorithms including supervised, unsupervised, and reinforcement learning techniques. Students explore neural networks, convolutional architectures, recurrent networks, transformers, natural language processing (NLP), computer vision, and generative models. The course emphasizes practical implementation using Python libraries such as TensorFlow, PyTorch, Scikit-Learn, and Keras.
2. Cybersecurity and Ethical Hacking
This course introduces students to the fundamentals of network security, cryptography, digital forensics, penetration testing, vulnerability assessment, secure software development practices, and compliance frameworks like ISO/IEC 27001 and NIST SP 800-53. Students learn to identify, analyze, and mitigate cybersecurity threats using tools such as Metasploit, Wireshark, Nessus, Burp Suite, and Kali Linux.
3. Big Data Analytics and Hadoop Ecosystem
This course focuses on handling large-scale datasets using big data technologies such as Apache Spark, Hadoop Distributed File System (HDFS), MapReduce, Hive, Pig, Kafka, and Storm. Students gain hands-on experience with real-world case studies involving data ingestion, processing, transformation, storage, and visualization using tools like Tableau, Power BI, and Python-based libraries.
4. Cloud Computing Architecture
This course explores cloud infrastructure models including public, private, hybrid, and multi-cloud environments. Students learn about virtualization technologies, containerization platforms (Docker, Kubernetes), serverless computing, microservices architecture, cloud security, and cost optimization strategies. Practical sessions involve configuring AWS, Microsoft Azure, Google Cloud Platform, and OpenStack.
5. Internet of Things (IoT) and Embedded Systems
This course covers the design and development of IoT applications using sensors, actuators, microcontrollers, wireless communication protocols (Wi-Fi, Bluetooth, LoRaWAN), edge computing, and cloud integration. Students work on projects involving smart home automation, industrial monitoring systems, environmental data collection, and wearable devices.
6. Software Project Management
This course provides comprehensive knowledge of software development life cycle (SDLC) methodologies including Agile, Scrum, Waterfall, DevOps, continuous integration/continuous deployment (CI/CD), risk management, quality assurance, team coordination, and project estimation techniques. Students engage in group projects simulating real-world software development environments.
7. Data Mining and Knowledge Discovery
This course teaches students to extract valuable insights from complex datasets using statistical methods, clustering algorithms, association rule mining, classification trees, decision forests, neural networks, and ensemble techniques. Tools like Weka, RapidMiner, KNIME, and Python libraries are utilized for practical exercises.
8. Human-Computer Interaction (HCI)
This course explores user-centered design principles, usability testing, interaction design patterns, prototyping, accessibility standards, cognitive load theory, and design thinking methodologies. Students create interactive interfaces using Figma, Adobe XD, Sketch, and other UI/UX design tools.
9. Game Development and Multimedia Applications
This course introduces students to game development frameworks like Unity3D, Unreal Engine, game mechanics, animation techniques, sound synthesis, 3D modeling, and cross-platform publishing. Practical sessions include building mobile games, VR experiences, interactive simulations, and multimedia presentations.
10. Natural Language Processing (NLP) and Text Analytics
This course focuses on text processing, sentiment analysis, named entity recognition, topic modeling, language generation, machine translation, speech-to-text conversion, and chatbot development using NLP libraries such as NLTK, spaCy, Transformers, Hugging Face, and Stanza.
Project-Based Learning Philosophy
The department believes in experiential learning through project-based education. The curriculum integrates mini-projects throughout the academic journey to reinforce theoretical concepts and foster innovation.
Mini Projects (Years 1-3)
Students work on small-scale projects in their respective semesters, focusing on specific topics related to core subjects. These projects are evaluated based on creativity, technical implementation, documentation quality, presentation skills, and peer collaboration. Topics vary each semester but typically include web application development, mobile app creation, database design, network simulation, algorithmic problem solving, and data visualization.
Final Year Thesis/Capstone Project
The capstone project is the culmination of the undergraduate experience. Students select a research topic aligned with their specialization track or industry interest. They form teams, conduct literature review, propose methodology, develop prototype or solution, test results, document findings, and present to faculty panel and external experts. Projects are supervised by dedicated faculty mentors who guide students through every stage of development.
Project selection involves a proposal submission process where students submit their ideas for approval by departmental committee. Final projects are judged based on originality, technical depth, impact potential, documentation clarity, presentation effectiveness, and overall contribution to the field of computer applications.