Comprehensive Course Structure
The engineering program at Ram Krishna Dharmarth Foundation Rkdf University Ranchi is structured to provide a comprehensive and progressive learning experience. The curriculum is designed to build upon foundational knowledge and gradually introduce students to advanced topics and specialized areas within their chosen field of engineering.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | None |
| 1 | ENG104 | Introduction to Engineering Design | 2-0-2-3 | None |
| 1 | ENG105 | English for Engineers | 2-0-0-2 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Engineering Mechanics | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Electrical Circuits | 3-1-0-4 | ENG102 |
| 2 | ENG204 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG205 | Engineering Drawing | 2-0-2-3 | ENG104 |
| 3 | ENG301 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Control Systems | 3-1-0-4 | ENG203 |
| 3 | ENG303 | Material Science | 3-1-0-4 | ENG103 |
| 3 | ENG304 | Computer Programming | 2-0-2-3 | ENG105 |
| 3 | ENG305 | Engineering Economics | 3-1-0-4 | ENG201 |
| 4 | ENG401 | Advanced Mathematics | 3-1-0-4 | ENG201 |
| 4 | ENG402 | Electromagnetic Fields | 3-1-0-4 | ENG203 |
| 4 | ENG403 | Manufacturing Processes | 3-1-0-4 | ENG303 |
| 4 | ENG404 | Software Engineering | 3-1-0-4 | ENG304 |
| 4 | ENG405 | Project Management | 3-1-0-4 | ENG305 |
| 5 | ENG501 | Advanced Control Systems | 3-1-0-4 | ENG302 |
| 5 | ENG502 | Power Systems | 3-1-0-4 | ENG203 |
| 5 | ENG503 | Database Systems | 3-1-0-4 | ENG304 |
| 5 | ENG504 | Computer Vision | 3-1-0-4 | ENG301 |
| 5 | ENG505 | Research Methodology | 2-0-2-3 | ENG405 |
| 6 | ENG601 | Machine Learning | 3-1-0-4 | ENG301 |
| 6 | ENG602 | Deep Learning | 3-1-0-4 | ENG601 |
| 6 | ENG603 | Network Security | 3-1-0-4 | ENG404 |
| 6 | ENG604 | Embedded Systems | 3-1-0-4 | ENG403 |
| 6 | ENG605 | Capstone Project | 2-0-4-4 | ENG505 |
| 7 | ENG701 | Advanced Data Structures | 3-1-0-4 | ENG304 |
| 7 | ENG702 | Advanced Algorithms | 3-1-0-4 | ENG701 |
| 7 | ENG703 | Cloud Computing | 3-1-0-4 | ENG404 |
| 7 | ENG704 | Internet of Things | 3-1-0-4 | ENG403 |
| 7 | ENG705 | Research Internship | 2-0-4-4 | ENG505 |
| 8 | ENG801 | Advanced Artificial Intelligence | 3-1-0-4 | ENG601 |
| 8 | ENG802 | Neural Networks | 3-1-0-4 | ENG801 |
| 8 | ENG803 | Big Data Analytics | 3-1-0-4 | ENG701 |
| 8 | ENG804 | Robotics | 3-1-0-4 | ENG501 |
| 8 | ENG805 | Final Year Project | 2-0-6-6 | ENG705 |
Advanced Departmental Elective Courses
The department offers a wide range of advanced departmental elective courses that allow students to explore specialized areas within their field of engineering. These courses are designed to provide students with in-depth knowledge and practical skills in their chosen areas of interest.
One of the most popular elective courses is 'Machine Learning,' which covers topics such as supervised and unsupervised learning, neural networks, and deep learning algorithms. Students in this course learn to apply machine learning techniques to real-world problems and gain hands-on experience with popular frameworks such as TensorFlow and PyTorch.
'Deep Learning' is another advanced elective that delves into the intricacies of neural networks and their applications in various domains. Students study advanced architectures such as convolutional neural networks, recurrent neural networks, and transformers, and learn to implement these models using programming languages such as Python and R.
'Network Security' is a specialized course that focuses on the principles and practices of securing computer networks and systems. Students learn about encryption, authentication, and intrusion detection systems, and gain practical experience in defending against cyber attacks.
'Embedded Systems' is an elective that covers the design and implementation of systems that are embedded within larger devices or systems. Students learn to program microcontrollers, design hardware-software interfaces, and develop real-time systems for various applications.
'Computer Vision' is a course that explores the techniques and algorithms used in computer vision and image processing. Students study topics such as image segmentation, object detection, and recognition, and learn to implement these techniques using libraries such as OpenCV and MATLAB.
'Cloud Computing' is an elective that covers the fundamentals of cloud computing and its applications in various domains. Students learn about cloud architectures, virtualization, and distributed systems, and gain hands-on experience with popular cloud platforms such as AWS and Azure.
'Internet of Things (IoT)' is a course that explores the design and implementation of IoT systems and applications. Students study topics such as sensor networks, wireless communication, and data analytics, and learn to develop IoT applications using various platforms and tools.
'Advanced Data Structures' is a course that covers advanced data structures and algorithms used in computer science and engineering. Students study topics such as trees, graphs, and hash tables, and learn to implement these structures efficiently using programming languages such as C++ and Java.
'Advanced Algorithms' is a course that explores the design and analysis of complex algorithms. Students study topics such as dynamic programming, greedy algorithms, and graph algorithms, and learn to solve complex problems using these techniques.
'Big Data Analytics' is a course that covers the principles and techniques of big data analytics and its applications in various domains. Students learn about data mining, machine learning, and statistical analysis, and gain hands-on experience with big data platforms such as Hadoop and Spark.
'Robotics' is a course that explores the design and implementation of robotic systems and applications. Students study topics such as kinematics, control systems, and sensor integration, and learn to build and program robots using various platforms and tools.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around the idea that students learn best when they are actively engaged in solving real-world problems. This approach encourages students to think critically, collaborate effectively, and develop practical skills that are essential for success in the engineering field.
Mini-projects are an integral part of the program, typically undertaken during the second and third years of study. These projects are designed to help students apply the theoretical concepts they have learned in class to practical situations. Students work in teams to design, implement, and evaluate solutions to engineering problems, often in collaboration with industry partners.
The final-year thesis/capstone project is a comprehensive project that allows students to demonstrate their mastery of the field of engineering. This project is typically undertaken in the final year and involves extensive research, design, and implementation of a significant engineering solution. Students work closely with faculty mentors to develop their projects and receive guidance throughout the process.
The selection of projects and faculty mentors is based on the interests and career aspirations of the students. Students are encouraged to choose projects that align with their goals and provide them with valuable experience in their chosen field. The department provides a wide range of project topics to ensure that students can find projects that interest them and match their skills.