Curriculum Overview
The Bachelor of Technology program at A N A College of Engineering and Management Studies is structured over eight semesters, with a balanced mix of core engineering subjects, departmental electives, science electives, and laboratory courses. The curriculum is designed to provide students with a strong foundation in basic sciences followed by progressive specialization in their chosen branch.
Semester-wise Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | ENG101 | English for Engineering | 3-0-0-3 | - |
| I | MAT101 | Calculus and Differential Equations | 4-0-0-4 | - |
| I | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| I | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| I | CSE101 | Introduction to Programming | 2-0-2-3 | - |
| I | ENG102 | Engineering Drawing | 2-0-2-2 | - |
| I | L101 | Programming Lab | 0-0-2-1 | - |
| II | MAT102 | Linear Algebra and Probability | 3-0-0-3 | MAT101 |
| II | PHY102 | Electromagnetism and Waves | 3-0-0-3 | PHY101 |
| II | CSE102 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| II | CHM102 | Organic Chemistry | 3-0-0-3 | CHM101 |
| II | ENG103 | Engineering Mechanics | 3-0-0-3 | - |
| II | L102 | Data Structures Lab | 0-0-2-1 | CSE101 |
| III | MAT201 | Numerical Methods and Optimization | 3-0-0-3 | MAT102 |
| III | CSE201 | Digital Logic Design | 3-0-0-3 | - |
| III | ECE201 | Electrical Circuits and Networks | 3-0-0-3 | - |
| III | MCH201 | Thermodynamics | 3-0-0-3 | - |
| III | CIV201 | Building Materials and Construction | 3-0-0-3 | - |
| III | L201 | Digital Logic Lab | 0-0-2-1 | CSE102 |
| IV | MAT202 | Statistics and Stochastic Processes | 3-0-0-3 | MAT201 |
| IV | CSE202 | Database Management Systems | 3-0-0-3 | CSE102 |
| IV | ECE202 | Analog Electronics | 3-0-0-3 | ECE201 |
| IV | MCH202 | Fluid Mechanics | 3-0-0-3 | MCH201 |
| IV | CIV202 | Structural Analysis | 3-0-0-3 | CIV201 |
| IV | L202 | Electronics Lab | 0-0-2-1 | ECE201 |
| V | CSE301 | Operating Systems | 3-0-0-3 | CSE202 |
| V | ECE301 | Control Systems | 3-0-0-3 | ECE202 |
| V | MCH301 | Machine Design | 3-0-0-3 | MCH202 |
| V | CIV301 | Transportation Engineering | 3-0-0-3 | CIV202 |
| V | L301 | Operating Systems Lab | 0-0-2-1 | CSE301 |
| VI | CSE302 | Computer Networks | 3-0-0-3 | CSE301 |
| VI | ECE302 | Signal and Systems | 3-0-0-3 | ECE202 |
| VI | MCH302 | Manufacturing Processes | 3-0-0-3 | MCH301 |
| VI | CIV302 | Environmental Engineering | 3-0-0-3 | CIV301 |
| VI | L302 | Networks Lab | 0-0-2-1 | CSE302 |
| VII | CSE401 | Artificial Intelligence | 3-0-0-3 | CSE302 |
| VII | ECE401 | Embedded Systems | 3-0-0-3 | ECE302 |
| VII | MCH401 | Advanced Thermodynamics | 3-0-0-3 | MCH302 |
| VII | CIV401 | Geotechnical Engineering | 3-0-0-3 | CIV302 |
| VII | L401 | AI Lab | 0-0-2-1 | CSE401 |
| VIII | CSE402 | Capstone Project | 3-0-0-6 | - |
| VIII | ECE402 | Final Year Project | 3-0-0-6 | - |
| VIII | MCH402 | Project Management | 3-0-0-3 | - |
| VIII | CIV402 | Urban Planning | 3-0-0-3 | CIV401 |
| VIII | L402 | Final Year Project Lab | 0-0-2-1 | - |
Detailed Departmental Elective Courses
Advanced Machine Learning (CSE403)
This course builds upon foundational knowledge in machine learning and delves into advanced topics such as reinforcement learning, deep learning architectures, generative adversarial networks (GANs), and neural architecture search. Students will explore the theoretical underpinnings of these methods while implementing them using frameworks like TensorFlow and PyTorch.
Quantum Computing Fundamentals (CSE404)
This elective introduces students to the principles of quantum mechanics and how they apply to computing. Topics include qubit manipulation, quantum algorithms, error correction, and quantum cryptography. Students will gain hands-on experience using simulators like Qiskit and Cirq.
Internet of Things (IoT) Security (CSE405)
This course focuses on securing IoT devices and networks against cyber threats. It covers encryption protocols, secure communication channels, authentication mechanisms, and privacy-preserving techniques. Students will implement security measures in real-world scenarios using tools like OpenSSL and Zephyr OS.
Blockchain Technologies (CSE406)
This course explores the fundamentals of blockchain technology, including consensus algorithms, smart contracts, decentralized applications (dApps), and cryptographic hashing. Students will develop blockchain-based solutions for various domains such as supply chain management and digital identity verification.
Cybersecurity Governance and Risk Management (CSE407)
This course addresses the governance aspects of cybersecurity, including risk assessment methodologies, compliance frameworks, and incident response strategies. Students will learn to design security policies aligned with industry standards like ISO 27001 and NIST SP 800-53.
Advanced Data Mining (CSE408)
This elective focuses on advanced data mining techniques such as clustering, association rule mining, anomaly detection, and text mining. Students will utilize tools like Weka, R, and Python libraries to analyze large datasets and extract meaningful insights.
Neural Networks for Natural Language Processing (CSE409)
This course explores the use of neural networks in processing natural language, including word embeddings, transformers, and sequence-to-sequence models. Students will implement NLP pipelines using Hugging Face Transformers and spaCy.
Computational Biology (CSE410)
This course bridges computer science and biology by applying computational methods to biological problems. Topics include genome assembly, protein structure prediction, gene expression analysis, and phylogenetic tree construction. Students will work with bioinformatics databases like NCBI and UniProt.
Computer Vision and Image Processing (CSE411)
This course covers the fundamentals of image processing and computer vision algorithms. It includes topics such as edge detection, feature extraction, object recognition, and segmentation. Students will implement vision systems using OpenCV and TensorFlow.
Software Engineering Practices (CSE412)
This elective emphasizes modern software engineering practices including agile development, DevOps pipelines, testing frameworks, and version control systems. Students will collaborate on real-world projects using Git, Jenkins, and Docker.
Project-Based Learning Philosophy
At A N A College, project-based learning is central to our pedagogical approach. We believe that hands-on experience is essential for developing problem-solving skills and applying theoretical knowledge in practical contexts.
Mini-Projects (First Year)
In the first year, students undertake mini-projects designed to familiarize them with basic engineering concepts and tools. These projects are typically completed in small teams and involve real-world problems such as designing a simple robotic arm or analyzing traffic patterns in urban areas.
Final-Year Thesis/Capstone Project
The final year capstone project is an intensive, multi-semester endeavor that allows students to synthesize their learning into a substantial contribution to their field. Students work closely with faculty mentors to identify research questions, gather data, and develop innovative solutions.
Students are encouraged to select projects aligned with their interests and career goals. They may collaborate with industry partners or government agencies on applied research initiatives that address societal challenges.
Evaluation Criteria
Projects are evaluated based on several criteria, including conceptual clarity, technical execution, innovation, presentation quality, and teamwork effectiveness. Each stage of the project lifecycle is assessed through peer reviews, faculty feedback, and final presentations to an external panel.
Students are also required to document their progress in detailed reports and maintain logs of experimental procedures, results, and lessons learned. This documentation serves as a foundation for future research and professional development.