Comprehensive Course Structure

The engineering curriculum at Dev Bhoomi Uttarakhand University Dehradun is meticulously designed to ensure a balanced progression from foundational science to advanced specialization. The program spans eight semesters with carefully curated core courses, departmental electives, science electives, and laboratory components.

Semester	Course Code	Full Course Title	Credit Structure (L-T-P-C)	Pre-requisites
Semester I	PHYS101	Physics for Engineers	3-1-0-4	-
	MATH101	Calculus and Differential Equations	3-1-0-4	-
	CHEM101	Chemistry for Engineers	3-1-0-4	-
	ENG101	Engineering Graphics	2-0-2-3	-
	COM101	Communication Skills	2-0-0-2	-
	EE101	Basic Electrical Engineering	3-1-0-4	-
	CS101	Introduction to Programming	2-0-2-3	-
	PHY101	Basic Physics Lab	0-0-3-2	-
	CHEM101	Chemistry Lab	0-0-3-2	-
	ENG101	Engineering Graphics Lab	0-0-3-2	-
	CS101	Programming Lab	0-0-3-2	-
	ME101	Engineering Workshop	0-0-6-4	-
Semester II	MATH201	Linear Algebra and Numerical Methods	3-1-0-4	MATH101
	PHYS201	Modern Physics	3-1-0-4	PHYS101
	EE201	Circuit Analysis	3-1-0-4	EE101
	CS201	Data Structures and Algorithms	3-1-0-4	CS101
	CHEM201	Organic Chemistry	3-1-0-4	CHEM101
	ME201	Mechanics of Materials	3-1-0-4	-
	CS202	Object-Oriented Programming	2-0-2-3	CS101
	MATH201	Numerical Methods Lab	0-0-3-2	-
	EE201	Circuit Analysis Lab	0-0-3-2	-
	CS201	Data Structures Lab	0-0-3-2	-
	ME201	Mechanics of Materials Lab	0-0-3-2	-
	CS202	OOP Lab	0-0-3-2	-
Semester III	MATH301	Probability and Statistics	3-1-0-4	MATH201
	CS301	Digital Logic Design	3-1-0-4	CS201
	EE301	Electromagnetic Fields	3-1-0-4	EE201
	ME301	Thermodynamics	3-1-0-4	ME201
	CHEM301	Physical Chemistry	3-1-0-4	CHEM201
	CS302	Database Management Systems	3-1-0-4	CS201
	ME302	Mechanical Measurements	2-1-0-3	ME201
	EE301	EMF Lab	0-0-3-2	-
	CS301	Digital Logic Design Lab	0-0-3-2	-
	ME301	Thermodynamics Lab	0-0-3-2	-
	CS302	DBMS Lab	0-0-3-2	-
	ME302	Mechanical Measurements Lab	0-0-3-2	-
Semester IV	MATH401	Advanced Mathematics	3-1-0-4	MATH301
	CS401	Operating Systems	3-1-0-4	CS302
	EE401	Control Systems	3-1-0-4	EE301
	ME401	Mechanics of Machines	3-1-0-4	ME301
	CHEM401	Chemical Kinetics	3-1-0-4	CHEM301
	CS402	Computer Networks	3-1-0-4	CS301
	ME402	Heat Transfer	3-1-0-4	ME301
	EE401	Control Systems Lab	0-0-3-2	-
	CS401	OS Lab	0-0-3-2	-
	ME401	Mechanics of Machines Lab	0-0-3-2	-
	CS402	Computer Networks Lab	0-0-3-2	-
	ME402	Heat Transfer Lab	0-0-3-2	-
Semester V	CS501	Machine Learning	3-1-0-4	CS401
	EE501	Power Electronics	3-1-0-4	EE401
	ME501	Manufacturing Processes	3-1-0-4	ME402
	CHEM501	Industrial Chemistry	3-1-0-4	CHEM401
	CS502	Web Technologies	3-1-0-4	CS402
	ME502	Fluid Mechanics	3-1-0-4	ME401
	EE502	Signal Processing	3-1-0-4	EE401
	CS501	ML Lab	0-0-3-2	-
	EE501	Power Electronics Lab	0-0-3-2	-
	ME501	Manufacturing Processes Lab	0-0-3-2	-
	CS502	Web Technologies Lab	0-0-3-2	-
	ME502	Fluid Mechanics Lab	0-0-3-2	-
Semester VI	CS601	Deep Learning	3-1-0-4	CS501
	EE601	Power Systems	3-1-0-4	EE501
	ME601	Design of Machine Elements	3-1-0-4	ME502
	CHEM601	Biochemistry	3-1-0-4	CHEM501
	CS602	Mobile Application Development	3-1-0-4	CS502
	ME602	Advanced Thermodynamics	3-1-0-4	ME502
	EE602	Digital Signal Processing	3-1-0-4	EE502
	CS601	Deep Learning Lab	0-0-3-2	-
	EE601	Power Systems Lab	0-0-3-2	-
	ME601	Design Lab	0-0-3-2	-
	CS602	Mobile App Development Lab	0-0-3-2	-
	ME602	Advanced Thermodynamics Lab	0-0-3-2	-
Semester VII	CS701	Artificial Intelligence	3-1-0-4	CS601
	EE701	Microprocessors and Microcontrollers	3-1-0-4	EE601
	ME701	Automotive Engineering	3-1-0-4	ME601
	CHEM701	Environmental Chemistry	3-1-0-4	CHEM601
	CS702	Cloud Computing	3-1-0-4	CS602
	ME702	Renewable Energy Systems	3-1-0-4	ME602
	EE702	Embedded Systems	3-1-0-4	EE602
	CS701	AI Lab	0-0-3-2	-
	EE701	Microprocessor Lab	0-0-3-2	-
	ME701	Automotive Engineering Lab	0-0-3-2	-
	CS702	Cloud Computing Lab	0-0-3-2	-
	ME702	Renewable Energy Systems Lab	0-0-3-2	-
Semester VIII	CS801	Capstone Project	3-0-6-9	All previous courses
	EE801	Advanced Control Systems	3-1-0-4	EE701
	ME801	Project Management	3-1-0-4	ME701
	CHEM801	Advanced Materials	3-1-0-4	CHEM701
	CS802	Blockchain Technologies	3-1-0-4	CS702
	ME802	Smart Manufacturing	3-1-0-4	ME702
	EE802	Power Quality and Reliability	3-1-0-4	EE702
	CS801	Capstone Project Lab	0-0-9-6	-
	EE801	Advanced Control Systems Lab	0-0-3-2	-
	ME801	Project Management Lab	0-0-3-2	-
	CS802	Blockchain Lab	0-0-3-2	-
	ME802	Smart Manufacturing Lab	0-0-3-2	-

Detailed Departmental Elective Courses

• Machine Learning: This course introduces students to fundamental algorithms and techniques in machine learning including supervised, unsupervised, and reinforcement learning. Students will implement models using Python libraries like Scikit-learn, TensorFlow, and PyTorch.
• Deep Learning: Advanced concepts in neural networks, convolutional networks, recurrent networks, and transformers are covered. Students work on real-world projects involving image recognition, natural language processing, and generative modeling.
• Cybersecurity: Covers network security protocols, cryptography, penetration testing, and ethical hacking. Students gain hands-on experience with tools like Wireshark, Kali Linux, and Metasploit.
• Embedded Systems: Focuses on designing embedded systems using microcontrollers, real-time operating systems, and IoT technologies. Practical sessions include developing firmware for ARM-based platforms.
• Renewable Energy Systems: Students learn about solar power generation, wind turbines, hydroelectric systems, and energy storage solutions. The course includes lab work on designing and testing renewable energy setups.
• Robotics and Automation: Covers robot kinematics, control systems, sensor integration, and automation principles. Students build robots capable of performing tasks autonomously or under human supervision.
• Data Science and Analytics: Introduces data visualization, statistical inference, predictive modeling, and big data analytics using tools like R, Python, and SQL.
• Power Systems Engineering: Explores power generation, transmission, distribution, and protection systems. Students analyze system stability and reliability under different operating conditions.
• Biomedical Engineering: Focuses on medical device design, bioinstrumentation, and biocompatibility. Projects involve developing assistive technologies for patients with disabilities.
• Smart Manufacturing: Covers Industry 4.0 technologies including IoT, digital twins, automation, and predictive maintenance in manufacturing environments.

Project-Based Learning Philosophy

The department believes that learning is most effective when students are actively engaged in solving real-world problems. Project-based learning is integrated throughout the curriculum to promote critical thinking, creativity, and teamwork. Each student undertakes a series of mini-projects during their academic journey, culminating in a final-year thesis or capstone project.

Mini-Projects Structure

Mini-projects are assigned at regular intervals throughout the program, typically lasting 6-8 weeks. These projects allow students to apply theoretical knowledge to practical challenges while working in teams of 3-5 members. Faculty mentors guide students through each stage of the project lifecycle from ideation to execution and presentation.

Final-Year Thesis/Capstone Project

The final-year project is a significant undertaking that spans 6 months and requires students to identify a relevant problem, design a solution, develop it, test its effectiveness, and present findings. Projects are often aligned with industry needs or faculty research initiatives. Students receive guidance from one or more mentors based on their area of interest.

Project Selection Process

Students can choose projects from available faculty research areas or propose their own ideas after consultation with mentors. The selection process involves a proposal submission, review by the departmental committee, and final approval based on feasibility and relevance to current trends in engineering.