Course Curriculum Overview
The curriculum for the Bachelor of Technology in Engineering program at Capital University Koderma is designed to provide a comprehensive foundation in engineering principles while offering flexibility to explore specialized areas. The program spans eight semesters, with each semester carrying 15-16 credits across core subjects, departmental electives, science electives, and laboratory components.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Introduction to Engineering | 3-0-0-3 | - |
| 1 | MAT101 | Calculus I | 4-0-0-4 | - |
| 1 | MAT102 | Linear Algebra and Differential Equations | 4-0-0-4 | MAT101 |
| 1 | PHY101 | Physics I | 3-0-0-3 | - |
| 1 | CHM101 | Chemistry | 3-0-0-3 | - |
| 1 | BIO101 | Basic Biology | 2-0-0-2 | - |
| 1 | CSE101 | Programming Fundamentals | 2-0-2-4 | - |
| 1 | LIT101 | English Communication | 2-0-0-2 | - |
| 2 | MAT201 | Calculus II | 4-0-0-4 | MAT101 |
| 2 | PHY201 | Physics II | 3-0-0-3 | PHY101 |
| 2 | CSE201 | Data Structures & Algorithms | 3-0-0-3 | CSE101 |
| 2 | ECE201 | Electrical Circuits and Networks | 3-0-0-3 | - |
| 2 | MEE201 | Applied Mechanics | 3-0-0-3 | - |
| 2 | CHE201 | Chemical Processes | 3-0-0-3 | - |
| 2 | LIT201 | Technical Writing | 2-0-0-2 | - |
| 3 | MAT301 | Probability and Statistics | 3-0-0-3 | MAT201 |
| 3 | CSE301 | Database Management Systems | 3-0-0-3 | CSE201 |
| 3 | ECE301 | Electromagnetic Fields and Waves | 3-0-0-3 | ECE201 |
| 3 | MEE301 | Mechanics of Materials | 3-0-0-3 | MEE201 |
| 3 | CHE301 | Chemical Reaction Engineering | 3-0-0-3 | CHE201 |
| 3 | CSE302 | Computer Organization and Architecture | 3-0-0-3 | CSE201 |
| 4 | MAT401 | Numerical Methods | 3-0-0-3 | MAT201 |
| 4 | CSE401 | Operating Systems | 3-0-0-3 | CSE301 |
| 4 | ECE401 | Signals and Systems | 3-0-0-3 | ECE201 |
| 4 | MEE401 | Thermodynamics | 3-0-0-3 | - |
| 4 | CHE401 | Mass Transfer Operations | 3-0-0-3 | CHE301 |
| 4 | CSE402 | Software Engineering | 3-0-0-3 | CSE301 |
| 5 | CSE501 | Machine Learning Fundamentals | 3-0-0-3 | CSE301 |
| 5 | ECE501 | Control Systems | 3-0-0-3 | ECE401 |
| 5 | MEE501 | Fluid Mechanics | 3-0-0-3 | - |
| 5 | CHE501 | Process Control | 3-0-0-3 | CHE401 |
| 5 | CSE502 | Web Technologies | 3-0-0-3 | CSE401 |
| 6 | CSE601 | Deep Learning | 3-0-0-3 | CSE501 |
| 6 | ECE601 | Microprocessors and Microcontrollers | 3-0-0-3 | ECE501 |
| 6 | MEE601 | Design of Machine Elements | 3-0-0-3 | MEE501 |
| 6 | CHE601 | Environmental Impact Assessment | 3-0-0-3 | - |
| 6 | CSE602 | Distributed Systems | 3-0-0-3 | CSE401 |
| 7 | CSE701 | Advanced Artificial Intelligence | 3-0-0-3 | CSE601 |
| 7 | ECE701 | Antenna and Wave Propagation | 3-0-0-3 | ECE601 |
| 7 | MEE701 | Advanced Mechanics of Materials | 3-0-0-3 | MEE601 |
| 7 | CHE701 | Chemical Engineering Design | 3-0-0-3 | CHE601 |
| 7 | CSE702 | Blockchain Technologies | 3-0-0-3 | CSE602 |
| 8 | CSE801 | Capstone Project | 4-0-0-4 | - |
| 8 | ECE801 | Industrial Project | 4-0-0-4 | - |
| 8 | MEE801 | Final Year Design Project | 4-0-0-4 | - |
| 8 | CHE801 | Research Thesis | 4-0-0-4 | - |
| 8 | CSE802 | Entrepreneurship in Tech | 2-0-0-2 | - |
Advanced Departmental Electives Overview
The department offers a range of advanced departmental electives that allow students to deepen their understanding in specific areas of interest. These courses are designed to be rigorous and practical, often incorporating real-world case studies and industry applications.
Machine Learning Fundamentals (CSE501)
This course introduces students to fundamental concepts in machine learning including supervised and unsupervised learning techniques, neural networks, decision trees, clustering algorithms, and reinforcement learning. Students will gain hands-on experience with popular frameworks such as TensorFlow and PyTorch through practical assignments.
Deep Learning (CSE601)
Building upon foundational knowledge in machine learning, this course explores advanced topics such as convolutional neural networks, recurrent neural networks, transformer architectures, generative adversarial networks, and natural language processing. Students will work on complex projects involving image classification, object detection, and sequence modeling.
Software Engineering (CSE402)
This course covers the entire software development lifecycle from requirements gathering to deployment and maintenance. Topics include software design patterns, version control systems, testing methodologies, agile development practices, and project management techniques. Students will collaborate on large-scale projects simulating real-world software environments.
Web Technologies (CSE502)
Students learn modern web development practices including HTML/CSS/JavaScript frameworks, responsive design principles, RESTful APIs, database integration, and security considerations. The course includes building full-stack applications using Node.js, React, and MongoDB.
Distributed Systems (CSE602)
This advanced elective focuses on distributed computing concepts including cloud architecture, microservices, containerization technologies like Docker and Kubernetes, load balancing, and fault tolerance mechanisms. Students will implement scalable systems using real-world platforms such as AWS or Google Cloud.
Blockchain Technologies (CSE702)
The course explores blockchain fundamentals, smart contracts, cryptocurrency applications, consensus algorithms, and decentralized applications (dApps). Practical components involve building simple blockchains, deploying smart contracts on Ethereum, and understanding regulatory implications of blockchain adoption.
Control Systems (ECE501)
This course covers mathematical modeling of dynamic systems, transfer functions, time-domain and frequency-domain analysis, stability criteria, root locus techniques, and state-space representation. Students will apply these concepts to analyze and design control systems for various engineering applications.
Microprocessors and Microcontrollers (ECE601)
Students study architecture and programming of microcontrollers such as ARM Cortex-M series and AVR microcontrollers. The course includes practical labs involving embedded system development, real-time operating systems, sensor interfacing, and communication protocols like I2C and SPI.
Thermodynamics (MEE401)
This course provides a comprehensive understanding of thermodynamic principles including energy conversion processes, heat engines, refrigeration cycles, and property relations. Students will solve complex problems involving ideal and real gas mixtures, phase equilibrium, and entropy changes.
Design of Machine Elements (MEE601)
The course covers stress analysis, failure theories, fatigue design, bolted joints, weldments, shaft design, gear systems, and bearing selection. Practical components include finite element analysis using ANSYS software and mechanical design projects based on industry standards.
Chemical Reaction Engineering (CHE301)
This course focuses on reactor design principles including batch reactors, continuous stirred tank reactors, plug flow reactors, and packed bed reactors. Students will learn to analyze reaction kinetics, perform stoichiometric calculations, and optimize process parameters for industrial applications.
Environmental Impact Assessment (CHE601)
The course teaches students how to evaluate potential environmental consequences of proposed projects using systematic methodologies such as HIA (Habitat Impact Assessment) and EIA (Environmental Impact Assessment). Practical components involve site visits, data collection, and report writing.
Project-Based Learning Philosophy
At Capital University Koderma, project-based learning is central to our educational philosophy. We believe that students learn best when they are actively engaged in solving real-world problems rather than passively absorbing information from textbooks or lectures.
The program incorporates mandatory mini-projects throughout the curriculum to reinforce theoretical concepts and develop practical skills. These projects are typically completed in teams of 3-5 students under faculty supervision, encouraging collaboration, communication, and leadership development.
Each project follows a structured approach involving problem identification, research, design, implementation, testing, and documentation phases. Students must present their findings to peers and faculty members, receiving feedback that helps improve both technical and presentation skills.
The final-year capstone project represents the culmination of all learning experiences gained during the program. Students select projects aligned with their interests and career goals, often in collaboration with industry partners or research mentors. The project spans multiple semesters and requires extensive documentation, including a detailed report, poster presentation, and oral defense.
Faculty mentors play a crucial role in guiding students through the project process, ensuring that they meet academic standards while encouraging innovation and creativity. Regular progress reviews, milestone evaluations, and peer assessments help maintain quality and accountability throughout the project lifecycle.