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.