Comprehensive Course Structure
The B.Tech Computer Applications program at Manav Rachna University Faridabad is structured into eight semesters, each designed to build upon the previous one while introducing new concepts and applications.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester I | CS101 | Programming in C | 3-0-0-3 | None |
| CS102 | Data Structures and Algorithms | 3-0-0-3 | CS101 | |
| PH101 | Physics for Computer Science | 3-0-0-3 | None | |
| MA101 | Engineering Mathematics I | 3-0-0-3 | None | |
| CH101 | Chemistry for Engineers | 3-0-0-3 | None | |
| HS101 | English Communication | 3-0-0-3 | None | |
| EC101 | Electronics Fundamentals | 3-0-0-3 | None | |
| CS103 | Computer Organization and Architecture | 3-0-0-3 | CS101 | |
| CS104 | Introduction to Problem Solving | 0-0-2-1 | None | |
| PH102 | Lab: Physics for Computer Science | 0-0-2-1 | PH101 | |
| Semester II | CS201 | Object-Oriented Programming with C++ | 3-0-0-3 | CS101 |
| CS202 | Database Management Systems | 3-0-0-3 | CS102 | |
| MA201 | Engineering Mathematics II | 3-0-0-3 | MA101 | |
| PH201 | Physics for Computer Science II | 3-0-0-3 | PH101 | |
| EC201 | Digital Electronics | 3-0-0-3 | EC101 | |
| HS201 | Communication Skills | 3-0-0-3 | HS101 | |
| CS203 | Operating Systems | 3-0-0-3 | CS103 | |
| CS204 | Computer Networks | 3-0-0-3 | CS103 | |
| CS205 | Software Engineering | 3-0-0-3 | CS201 | |
| CS206 | Lab: C++ and Data Structures | 0-0-2-1 | CS101, CS102 | |
| Semester III | CS301 | Advanced Data Structures | 3-0-0-3 | CS102, CS201 |
| CS302 | Microprocessor and Microcontroller | 3-0-0-3 | EC201 | |
| MA301 | Probability and Statistics | 3-0-0-3 | MA201 | |
| CS303 | Web Technologies | 3-0-0-3 | CS201 | |
| CS304 | Database Design and Management | 3-0-0-3 | CS202 | |
| CS305 | Computer Graphics | 3-0-0-3 | CS102, CS201 | |
| CS306 | Artificial Intelligence | 3-0-0-3 | CS301 | |
| CS307 | Machine Learning | 3-0-0-3 | MA301, CS301 | |
| CS308 | Lab: Web and Mobile Development | 0-0-2-1 | CS303 | |
| CS309 | Lab: Database Systems | 0-0-2-1 | CS202 | |
| Semester IV | CS401 | Cybersecurity Fundamentals | 3-0-0-3 | CS204 |
| CS402 | Big Data Analytics | 3-0-0-3 | MA301, CS301 | |
| CS403 | Cloud Computing | 3-0-0-3 | CS204 | |
| CS404 | Mobile Application Development | 3-0-0-3 | CS303 | |
| CS405 | Internet of Things | 3-0-0-3 | CS201 | |
| CS406 | Data Mining and Warehousing | 3-0-0-3 | MA301, CS301 | |
| CS407 | Software Testing | 3-0-0-3 | CS205 | |
| CS408 | Lab: Cybersecurity and Networking | 0-0-2-1 | CS401 | |
| CS409 | Lab: IoT and Cloud Platforms | 0-0-2-1 | CS405, CS403 | |
| CS410 | Mini Project I | 0-0-0-3 | CS205, CS303 | |
| Semester V | CS501 | Advanced Machine Learning | 3-0-0-3 | CS307 |
| CS502 | Natural Language Processing | 3-0-0-3 | CS501 | |
| CS503 | Computer Vision | 3-0-0-3 | CS501 | |
| CS504 | Deep Learning | 3-0-0-3 | CS501 | |
| CS505 | Reinforcement Learning | 3-0-0-3 | CS501 | |
| CS506 | Information Security | 3-0-0-3 | CS401 | |
| CS507 | Network Security | 3-0-0-3 | CS204, CS401 | |
| CS508 | Mobile Computing | 3-0-0-3 | CS404 | |
| CS509 | Lab: AI and ML Projects | 0-0-2-1 | CS501, CS502 | |
| CS510 | Mini Project II | 0-0-0-3 | CS410 | |
| Semester VI | CS601 | Software Architecture and Design Patterns | 3-0-0-3 | CS205 |
| CS602 | DevOps and CI/CD | 3-0-0-3 | CS403 | |
| CS603 | Advanced Cloud Technologies | 3-0-0-3 | CS403 | |
| CS604 | Big Data Technologies | 3-0-0-3 | CS402 | |
| CS605 | Distributed Systems | 3-0-0-3 | CS204 | |
| CS606 | Advanced Cybersecurity Techniques | 3-0-0-3 | CS506 | |
| CS607 | Quantitative Finance and Risk Modeling | 3-0-0-3 | MA301 | |
| CS608 | Lab: Advanced Cloud and DevOps | 0-0-2-1 | CS602, CS603 | |
| CS609 | Lab: Security Projects | 0-0-2-1 | CS606 | |
| CS610 | Mini Project III | 0-0-0-3 | CS510 | |
| Semester VII | CS701 | Research Methodology | 3-0-0-3 | CS301 |
| CS702 | Capstone Project Proposal | 3-0-0-3 | CS610 | |
| CS703 | Advanced Topics in Computer Science | 3-0-0-3 | CS501 | |
| CS704 | Entrepreneurship and Innovation | 3-0-0-3 | None | |
| CS705 | Industry Internship | 0-0-0-6 | CS610 | |
| CS706 | Capstone Project Implementation | 0-0-0-9 | CS702 | |
| CS707 | Advanced Capstone Project Lab | 0-0-2-3 | CS706 | |
| CS708 | Professional Ethics and Social Responsibility | 3-0-0-3 | None | |
| CS709 | Lab: Capstone Implementation | 0-0-2-1 | CS706 | |
| CS710 | Capstone Project Defense | 0-0-0-3 | CS706 | |
| Semester VIII | CS801 | Thesis Writing and Presentation Skills | 3-0-0-3 | CS702 |
| CS802 | Research Thesis | 0-0-0-12 | CS706 | |
| CS803 | Advanced Capstone Project | 0-0-0-9 | CS706 | |
| CS804 | Capstone Project Final Presentation | 0-0-0-3 | CS803 | |
| CS805 | Professional Development Workshop | 3-0-0-3 | None | |
| CS806 | Placement Preparation Training | 3-0-0-3 | None | |
| CS807 | Industry Interaction Sessions | 3-0-0-3 | None | |
| CS808 | Final Internship Report | 0-0-0-6 | CS705 | |
| CS809 | Lab: Final Capstone Project | 0-0-2-1 | CS803 | |
| CS810 | Graduation Ceremony and Alumni Networking | 0-0-0-3 | None |
Advanced Departmental Electives
The department offers a wide range of advanced elective courses that allow students to specialize in specific areas based on their interests and career goals. These courses are designed to provide in-depth knowledge and practical experience in cutting-edge technologies:
- Deep Learning and Neural Networks: This course covers the theory and application of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement real-world projects involving image recognition, natural language processing, and time series forecasting.
- Reinforcement Learning: Focused on developing intelligent agents that learn optimal behaviors through interaction with an environment, this course explores Markov decision processes, Q-learning, policy gradients, and actor-critic methods. Students will apply these concepts to robotics, game playing, and autonomous systems.
- Natural Language Processing (NLP): This elective delves into the computational analysis of human language using machine learning techniques. Topics include text classification, sentiment analysis, named entity recognition, and sequence-to-sequence models for translation and summarization tasks.
- Computer Vision: Covering image processing, object detection, segmentation, and recognition algorithms, this course introduces students to state-of-the-art architectures like ResNet, YOLO, and GANs. Practical applications include medical imaging, surveillance systems, and augmented reality.
- Advanced Cybersecurity: Addressing modern threats in network security, cryptography, and incident response, this course includes hands-on labs on penetration testing, malware analysis, and secure coding practices. Students will develop skills to protect critical infrastructure from cyber attacks.
- Big Data Technologies: Exploring frameworks like Hadoop, Spark, and Kafka for processing large datasets, this course covers distributed computing models, data streaming, and real-time analytics. Students will work on projects involving social media data analysis, sensor networks, and financial market prediction.
- Mobile Application Development: Focused on building cross-platform apps using frameworks like Flutter and React Native, this course covers UI/UX design principles, app deployment strategies, and backend integration. Projects include fitness trackers, educational platforms, and social networking applications.
- Cloud Computing and DevOps: This elective introduces students to cloud service models (IaaS, PaaS, SaaS), containerization using Docker and Kubernetes, CI/CD pipelines, and automation tools like Jenkins and Ansible. Students will deploy scalable applications in AWS, Azure, and GCP environments.
- Internet of Things (IoT): Covering embedded systems programming, wireless communication protocols, and smart city applications, this course explores sensor networks, edge computing, and real-time data processing for IoT devices. Projects include home automation systems, environmental monitoring platforms, and industrial IoT solutions.
- Data Science and Analytics: Focused on statistical modeling, data mining, visualization techniques, and predictive analytics using Python, R, and Tableau, this course includes hands-on labs on regression analysis, clustering algorithms, and machine learning models. Students will work with real datasets from various industries.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a core component of the educational experience. This approach encourages students to apply theoretical knowledge to solve real-world problems, fostering critical thinking and innovation.
Mini-projects are integrated throughout the curriculum starting from the second semester. These projects typically last 4-6 weeks and involve teams of 3-5 students working under faculty supervision. Students select topics aligned with their interests or current industry trends, ensuring relevance and engagement.
The final-year capstone project is a comprehensive endeavor that spans the entire seventh and eighth semesters. It involves developing an original solution to a complex problem identified by either the student, a faculty mentor, or an industry partner. The project must demonstrate mastery of both technical skills and project management capabilities.
Project selection is guided by faculty mentors who help students identify feasible yet challenging topics. Students are encouraged to collaborate with industry partners or research institutions, providing exposure to real-world constraints and expectations.
Evaluation criteria for projects include technical execution, innovation, presentation quality, documentation standards, and teamwork effectiveness. Regular progress reports and milestone reviews ensure timely completion and continuous improvement throughout the project lifecycle.