Course Structure Across 8 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Physics I | 3-1-0-4 | - |
| 1 | ENG103 | Engineering Chemistry I | 3-1-0-4 | - |
| 1 | ENG104 | Engineering Graphics & Design | 2-0-2-3 | - |
| 1 | ENG105 | Programming for Engineers | 2-0-2-3 | - |
| 1 | ENG106 | Introduction to Engineering | 2-0-0-2 | - |
| 1 | ENG107 | English for Engineers | 2-0-0-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Engineering Physics II | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Engineering Chemistry II | 3-1-0-4 | ENG103 |
| 2 | ENG204 | Electrical & Electronics Fundamentals | 3-1-0-4 | - |
| 2 | ENG205 | Computer Programming Lab | 0-0-3-1 | - |
| 2 | ENG206 | Engineering Mechanics | 3-1-0-4 | - |
| 2 | ENG207 | Technical Communication Skills | 2-0-0-2 | - |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Materials Science & Engineering | 3-1-0-4 | ENG203 |
| 3 | ENG303 | Thermodynamics & Heat Transfer | 3-1-0-4 | ENG202 |
| 3 | ENG304 | Fluid Mechanics & Hydraulic Machines | 3-1-0-4 | ENG202 |
| 3 | ENG305 | Digital Electronics & Logic Design | 3-1-0-4 | ENG204 |
| 3 | ENG306 | Control Systems & Instrumentation | 3-1-0-4 | - |
| 3 | ENG307 | Engineering Economics & Management | 2-0-0-2 | - |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Machine Design & Dynamics | 3-1-0-4 | ENG306 |
| 4 | ENG403 | Power Generation & Distribution | 3-1-0-4 | ENG204 |
| 4 | ENG404 | Signals & Systems | 3-1-0-4 | ENG301 |
| 4 | ENG405 | Computer Architecture & Microprocessors | 3-1-0-4 | ENG305 |
| 4 | ENG406 | Project Management & Risk Analysis | 2-0-0-2 | - |
| 5 | ENG501 | Advanced Mathematics for Engineers | 3-1-0-4 | ENG401 |
| 5 | ENG502 | Advanced Thermodynamics & Gas Turbines | 3-1-0-4 | ENG303 |
| 5 | ENG503 | Structural Analysis & Design | 3-1-0-4 | ENG206 |
| 5 | ENG504 | Signal Processing & Pattern Recognition | 3-1-0-4 | ENG404 |
| 5 | ENG505 | Software Engineering & Quality Assurance | 3-1-0-4 | ENG405 |
| 5 | ENG506 | Renewable Energy Systems | 3-1-0-4 | ENG303 |
| 5 | ENG507 | Research Methodology & Ethics | 2-0-0-2 | - |
| 6 | ENG601 | Advanced Control Systems | 3-1-0-4 | ENG306 |
| 6 | ENG602 | Mechanical Systems & Dynamics | 3-1-0-4 | ENG402 |
| 6 | ENG603 | Electrical Power Systems | 3-1-0-4 | ENG403 |
| 6 | ENG604 | Artificial Intelligence & Machine Learning | 3-1-0-4 | ENG504 |
| 6 | ENG605 | Data Mining & Big Data Analytics | 3-1-0-4 | ENG504 |
| 6 | ENG606 | Cybersecurity & Network Protection | 3-1-0-4 | ENG505 |
| 6 | ENG607 | Entrepreneurship & Innovation | 2-0-0-2 | - |
| 7 | ENG701 | Advanced Project Management | 3-1-0-4 | ENG506 |
| 7 | ENG702 | Robotics & Automation | 3-1-0-4 | ENG601 |
| 7 | ENG703 | Biomedical Engineering | 3-1-0-4 | ENG502 |
| 7 | ENG704 | Smart Grid Technologies | 3-1-0-4 | ENG603 |
| 7 | ENG705 | Advanced Materials & Nanotechnology | 3-1-0-4 | ENG202 |
| 7 | ENG706 | Quantitative Finance & Risk Modeling | 3-1-0-4 | ENG501 |
| 7 | ENG707 | Research Project I | 0-0-6-6 | - |
| 8 | ENG801 | Research Project II | 0-0-6-6 | ENG707 |
| 8 | ENG802 | Final Year Thesis | 0-0-6-6 | ENG707 |
| 8 | ENG803 | Industrial Training & Internship | 0-0-12-6 | - |
| 8 | ENG804 | Capstone Design Project | 0-0-12-6 | ENG707 |
Detailed Course Descriptions for Advanced Departmental Electives
Artificial Intelligence & Machine Learning: This course introduces students to fundamental concepts of artificial intelligence, including search algorithms, knowledge representation, planning, machine learning techniques, and neural networks. It emphasizes practical implementation using Python and TensorFlow.
Cybersecurity & Network Protection: Designed for students interested in protecting digital assets, this course covers network security protocols, cryptography, intrusion detection systems, and ethical hacking techniques. Students gain hands-on experience with industry-standard tools.
Renewable Energy Systems: This elective explores solar, wind, hydroelectric, and geothermal energy conversion technologies. Students learn about energy storage solutions, grid integration challenges, and policy frameworks supporting clean energy adoption.
Biomedical Engineering: Combining principles of engineering with medicine, this course covers bioinstrumentation, medical imaging, biomaterials, and computational biology. It includes laboratory sessions on designing prosthetics and analyzing physiological signals.
Data Mining & Big Data Analytics: This course teaches students how to extract meaningful patterns from large datasets using statistical methods, clustering algorithms, and predictive modeling techniques. Emphasis is placed on applying these skills in real-world scenarios.
Smart Grid Technologies: Focused on modernizing electrical power systems, this course covers smart meters, demand response systems, distributed energy resources, and grid stability optimization strategies.
Advanced Materials & Nanotechnology: Students explore the properties and applications of advanced materials such as graphene, carbon nanotubes, and shape-memory alloys. The course includes laboratory experiments on material synthesis and characterization techniques.
Quantitative Finance & Risk Modeling: This elective provides an introduction to financial markets, derivative pricing models, portfolio optimization, and risk management techniques using mathematical and computational tools.
Robotics & Automation: Covering both theoretical and practical aspects of robotics, this course includes topics such as kinematics, control systems, sensor integration, and autonomous navigation. Students build and program robots to perform specific tasks.
Advanced Control Systems: This course builds upon basic control theory by exploring advanced concepts such as state-space representation, robust control, adaptive control, and optimal control. It prepares students for designing complex systems in aerospace, automotive, and industrial applications.
Project-Based Learning Philosophy
At Annamacharya University Rajampet, project-based learning is a cornerstone of our engineering education. We believe that the most effective way to learn engineering is through active engagement with real-world problems. This philosophy drives our curriculum design, where students are encouraged to apply theoretical knowledge in practical scenarios from their first year onwards.
Mini-projects begin in the third semester and continue throughout the program. These projects are designed to be collaborative, interdisciplinary, and relevant to current industry challenges. Students work in teams under faculty supervision to develop solutions for specific problems related to their specialization or general engineering principles.
The final-year thesis/capstone project is a significant component of our program. It allows students to conduct independent research, explore emerging technologies, or address societal needs through innovative engineering solutions. Projects are selected based on student interests, faculty expertise, and industry relevance.
Students are guided by faculty mentors who provide technical support, feedback, and resources throughout the project lifecycle. Regular progress reviews ensure that projects stay on track and meet quality standards. The final presentation and documentation serve as a platform for students to showcase their work to peers, faculty, and industry partners.