Course Structure Overview
The curriculum for the Bachelor of Technology in Engineering at Dbs Global University Dehradun is designed to provide students with a strong foundation in fundamental sciences, followed by specialized knowledge in their chosen branch. The program spans eight semesters and includes core courses, departmental electives, science electives, and laboratory sessions.
First Year Core Courses
- Engineering Mathematics I
- Physics for Engineers
- Chemistry for Engineers
- Basic Electrical Engineering
- Computer Programming Fundamentals
- Engineering Drawing & Design
- Introduction to Engineering
- Workshop Practice
Second Year Core Courses
- Engineering Mathematics II
- Thermodynamics
- Strength of Materials
- Electrical Circuits and Machines
- Data Structures & Algorithms
- Signals and Systems
- Engineering Mechanics
- Fluid Mechanics
Third Year Core Courses
- Control Systems
- Power Electronics
- Materials Science
- Digital Signal Processing
- Computer Architecture
- Machine Design
- Environmental Engineering
- Operations Research
Fourth Year Core Courses
- Advanced Control Systems
- Power System Analysis
- Renewable Energy Technologies
- Embedded Systems
- Software Engineering
- Project Management
- Industrial Safety
- Capstone Project
Science Electives
- Statistics and Probability
- Mathematical Modeling
- Computational Methods
- Quantum Physics
- Biochemistry
- Geology for Engineers
- Optics and Photonics
- Acoustics and Vibration
Departmental Electives (Computer Science Engineering)
- Machine Learning
- Deep Learning
- Cloud Computing
- Database Systems
- Web Development
- Mobile Application Development
- Computer Vision
- Cybersecurity
Departmental Electives (Mechanical Engineering)
- Advanced Manufacturing Processes
- Finite Element Analysis
- Automotive Engineering
- Heat Transfer
- Manufacturing Automation
- Aerospace Engineering
- Robotics
- Energy Systems
Departmental Electives (Civil Engineering)
- Structural Dynamics
- Transportation Engineering
- Water Resources Engineering
- Construction Management
- Geotechnical Engineering
- Environmental Impact Assessment
- Smart Cities
- Sustainable Infrastructure
Laboratory Sessions
- Basic Lab Practices
- Electronics Laboratory
- Computer Laboratory
- Materials Testing Laboratory
- Thermodynamics Laboratory
- Control Systems Laboratory
- Power Electronics Laboratory
- Research and Development Lab
Advanced Departmental Electives
The department offers a wide range of advanced elective courses that allow students to specialize in areas of personal interest or career goals. These courses are taught by faculty members who are experts in their respective fields and often involve collaborative research projects with industry partners.
Machine Learning (CS)
This course introduces students to fundamental concepts of machine learning, including supervised and unsupervised learning algorithms, neural networks, decision trees, clustering techniques, and reinforcement learning. Students learn to implement these algorithms using Python libraries such as scikit-learn and TensorFlow.
The course emphasizes practical applications through real-world datasets and project-based assignments. Students work on end-to-end machine learning pipelines, from data preprocessing to model deployment in production environments.
Deep Learning (CS)
Deep learning is a subset of machine learning that uses multi-layered neural networks to process complex patterns in data. This course covers convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).
Students gain hands-on experience with frameworks like PyTorch and TensorFlow, implementing advanced architectures for image recognition, natural language processing, and time-series forecasting. The course includes a capstone project where students develop an original deep learning solution to a real-world problem.
Cloud Computing (CS)
This elective explores the architecture and implementation of cloud computing systems, including virtualization, containerization, microservices, and distributed computing models. Students learn to deploy applications on platforms such as AWS, Azure, and Google Cloud Platform.
The course includes practical labs where students design and deploy scalable web applications using cloud-native technologies. Topics include DevOps practices, infrastructure as code (IaC), and security considerations in cloud environments.
Database Systems (CS)
This course provides an in-depth understanding of database design, implementation, and management. Students study relational models, SQL queries, normalization techniques, indexing strategies, and transaction processing.
Advanced topics include NoSQL databases, distributed databases, data warehousing, and big data technologies such as Hadoop and Spark. Students engage in projects involving database schema design and optimization for performance-critical applications.
Web Development (CS)
The Web Development course covers modern frontend and backend technologies used in building responsive web applications. Students learn HTML5, CSS3, JavaScript frameworks like React and Angular, server-side scripting with Node.js, and RESTful API development.
Through hands-on labs, students build full-stack applications from scratch, integrating databases, authentication systems, and user interfaces. The course also addresses deployment strategies using platforms like Heroku, Docker, and cloud services.
Mobile Application Development (CS)
This elective focuses on developing cross-platform mobile applications using tools such as React Native, Flutter, and Xamarin. Students learn to design user interfaces for iOS and Android platforms, integrate APIs, handle offline data synchronization, and optimize app performance.
The course includes building real-world apps with features like push notifications, location services, and social media integration. Students also explore monetization strategies and app store optimization techniques.
Computer Vision (CS)
Computer vision involves teaching machines to interpret and understand visual information from the world. This course covers image processing techniques, feature extraction, object detection, segmentation, and recognition algorithms.
Students implement computer vision systems using OpenCV, TensorFlow, and PyTorch, tackling challenges such as facial recognition, autonomous navigation, and medical image analysis. The course culminates in a project where students develop a computer vision application for a specific domain.
Cybersecurity (CS)
This course explores the principles and practices of cybersecurity, including network security, cryptography, secure programming, and risk management. Students learn about common vulnerabilities such as SQL injection, cross-site scripting (XSS), and buffer overflow attacks.
Practical labs involve penetration testing using tools like Metasploit, Wireshark, and Burp Suite. The course includes topics on compliance standards, incident response planning, and ethical hacking methodologies. Students complete a capstone project focused on defending against advanced persistent threats (APTs).
Advanced Manufacturing Processes (ME)
This elective covers emerging manufacturing technologies such as 3D printing, laser cutting, CNC machining, and additive manufacturing. Students study material properties, process optimization, and quality control in modern manufacturing environments.
The course includes laboratory sessions where students operate advanced manufacturing equipment and design prototypes using CAD software. Projects involve developing innovative manufacturing solutions for aerospace, automotive, or consumer electronics industries.
Finite Element Analysis (ME)
Finite element analysis is a numerical method used to solve complex engineering problems involving stress, strain, heat transfer, and fluid dynamics. This course teaches students how to model and simulate physical phenomena using software tools like ANSYS and ABAQUS.
Students learn to discretize continuous systems into finite elements, apply boundary conditions, and interpret results. The course includes hands-on labs where students perform simulations for real-world engineering challenges such as bridge design or heat exchanger optimization.
Aerospace Engineering (ME)
This elective explores the principles of flight mechanics, propulsion systems, aerodynamics, and spacecraft design. Students study aircraft configurations, engine types, orbital mechanics, and satellite technology.
The course includes laboratory experiments involving wind tunnel testing, flight simulation software, and rocket propulsion systems. Projects involve designing and analyzing aircraft or spacecraft components under various operating conditions.
Heat Transfer (ME)
This course covers conduction, convection, and radiation heat transfer mechanisms in engineering systems. Students learn to solve transient and steady-state heat transfer problems using analytical methods and numerical simulations.
The course includes laboratory sessions where students measure thermal properties of materials and analyze heat exchanger performance. Projects involve designing efficient thermal management systems for electronic devices or industrial processes.
Manufacturing Automation (ME)
This elective introduces automation technologies used in modern manufacturing environments, including programmable logic controllers (PLCs), robotics, sensor integration, and industrial communication protocols.
Students learn to program robotic arms, design control systems for automated production lines, and integrate sensors with manufacturing equipment. Labs involve building and testing automated assembly systems using industry-standard tools like Siemens PLCs and RobotStudio.
Energy Systems (ME)
This course focuses on renewable energy technologies, energy storage systems, and power generation strategies. Students study solar photovoltaics, wind turbines, hydroelectric systems, fuel cells, and nuclear energy technologies.
The course includes laboratory experiments involving solar panel efficiency testing, battery charging systems, and grid integration challenges. Projects involve designing sustainable energy solutions for residential or commercial applications.
Structural Dynamics (CE)
This elective covers the behavior of structures under dynamic loads such as earthquakes, wind, and impact forces. Students learn to analyze structural response using time-domain and frequency-domain methods.
The course includes laboratory sessions involving vibration testing machines, modal analysis software, and seismic simulation equipment. Projects involve designing structures that can withstand extreme loading conditions while maintaining safety and functionality.
Transportation Engineering (CE)
This course explores transportation planning, traffic flow theory, road design, public transit systems, and urban mobility solutions. Students learn to model traffic networks, optimize routing algorithms, and evaluate infrastructure investments.
The course includes laboratory sessions using traffic simulation software such as VISSIM and SUMO. Projects involve designing efficient transportation networks for cities or evaluating the impact of new infrastructure projects on traffic patterns.
Water Resources Engineering (CE)
This elective covers water supply systems, wastewater treatment, flood management, irrigation engineering, and environmental impact assessment. Students study hydrological processes, water quality standards, and sustainable resource management strategies.
Laboratory sessions involve water sampling techniques, flow measurement methods, and pollution control technologies. Projects include designing water distribution networks for urban areas or developing stormwater management systems for environmentally sensitive regions.
Construction Management (CE)
This course provides an overview of construction project planning, scheduling, budgeting, risk assessment, and quality control. Students learn to manage construction activities from initial design through final delivery.
The course includes practical labs involving project management software like Primavera P6, Microsoft Project, and BIM modeling tools. Projects involve developing comprehensive construction plans for residential or commercial buildings with cost estimates and timeline analysis.
Geotechnical Engineering (CE)
This elective focuses on soil mechanics, foundation engineering, slope stability analysis, and underground construction techniques. Students study bearing capacity theories, consolidation processes, and geotechnical testing methods.
Laboratory sessions involve soil classification tests, triaxial compression experiments, and shear strength measurements. Projects include designing shallow and deep foundations for different geological conditions and assessing landslide risks in mountainous terrain.
Environmental Impact Assessment (CE)
This course addresses the process of evaluating environmental consequences of proposed development projects. Students learn to conduct baseline studies, predict impacts, propose mitigation measures, and prepare environmental impact statements.
The course includes case studies from real-world projects and hands-on experience with assessment software like AERMOD and CEA. Projects involve conducting EIAs for new industrial facilities or infrastructure developments and proposing sustainable alternatives.
Smart Cities (CE)
This elective explores the integration of digital technologies in urban planning and management. Students study smart transportation systems, intelligent energy grids, IoT-based monitoring networks, and citizen engagement platforms.
The course includes laboratory sessions involving simulation tools for urban modeling, data analytics for traffic optimization, and GIS mapping software for spatial analysis. Projects involve designing smart city initiatives that improve quality of life while reducing environmental impact.
Sustainable Infrastructure (CE)
This course focuses on designing infrastructure systems that minimize environmental footprint and promote long-term sustainability. Students study green building practices, lifecycle assessment methods, carbon accounting, and circular economy principles.
Laboratory sessions involve material selection for sustainable construction, energy efficiency calculations, and waste reduction strategies. Projects include developing sustainable designs for residential or commercial buildings that meet LEED or BREEAM certification standards.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a core component of engineering education. This pedagogical approach allows students to apply theoretical concepts in practical settings, fostering innovation and problem-solving skills essential for professional success.
Mini Projects (First Year)
In the first year, students undertake mini projects that introduce them to design thinking and engineering processes. These projects are typically interdisciplinary, combining elements from multiple branches of engineering to solve real-world problems. Examples include designing a simple robot, building a model bridge, or developing an energy-efficient lighting system.
Mini projects are evaluated based on creativity, technical execution, teamwork, and presentation skills. Students receive feedback from faculty mentors and peer reviews to refine their approach and improve outcomes.
Capstone Projects (Fourth Year)
The capstone project is the culmination of a student's engineering education. It involves developing a comprehensive solution to a complex problem in their chosen field, often with input from industry partners or research institutions.
Students select projects based on their interests and career goals, working closely with faculty mentors throughout the process. The project typically spans several months and includes literature review, design phase, prototyping, testing, and documentation.
Evaluation Criteria
Projects are evaluated using a rubric that assesses technical competency, innovation, teamwork, communication, and adherence to professional standards. Students present their projects at departmental symposiums, where they receive feedback from faculty, industry professionals, and peers.
The evaluation process encourages students to think critically about their work and consider alternative approaches or improvements. It also prepares them for real-world project management scenarios where collaboration and communication are key success factors.
Faculty Mentorship
Each student is assigned a faculty mentor who guides them through their projects, providing technical expertise, feedback on progress, and support in overcoming challenges. Mentors help students navigate complex concepts, suggest resources for research, and ensure that their projects align with industry standards.
Student Selection Process
Students are encouraged to explore different project ideas during the first semester and engage in discussions with faculty mentors to identify suitable topics. The selection process considers student interests, academic performance, availability of resources, and potential for innovation.
Projects may be individual or team-based, depending on complexity and scope. Teams are formed based on complementary skills and shared interests, ensuring effective collaboration and balanced workload distribution.