Comprehensive B.Tech Curriculum Overview
The Bachelor of Technology program at Truba College of Science and Technology is meticulously structured across eight semesters, with a blend of foundational science courses, core engineering subjects, departmental electives, and practical lab work. The curriculum emphasizes not only technical depth but also interdisciplinary exposure and real-world application.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | MAT101 | Mathematics I | 3-0-0-3 | - |
| I | PHY101 | Physics I | 3-0-0-3 | - |
| I | CHM101 | Chemistry I | 3-0-0-3 | - |
| I | ENG101 | English Communication | 2-0-0-2 | - |
| I | CSE101 | Introduction to Programming | 2-0-2-3 | - |
| I | CSE102 | Engineering Graphics | 2-0-0-2 | - |
| II | MAT102 | Mathematics II | 3-0-0-3 | MAT101 |
| II | PHY102 | Physics II | 3-0-0-3 | PHY101 |
| II | CHM102 | Chemistry II | 3-0-0-3 | CHM101 |
| II | CSE103 | Data Structures & Algorithms | 3-0-0-3 | CSE101 |
| II | CSE104 | Digital Logic Design | 3-0-0-3 | - |
| III | MAT201 | Mathematics III | 3-0-0-3 | MAT102 |
| III | CSE201 | Database Management Systems | 3-0-0-3 | CSE103 |
| III | CSE202 | Operating Systems | 3-0-0-3 | CSE104 |
| III | CSE203 | Computer Networks | 3-0-0-3 | CSE104 |
| IV | MAT202 | Mathematics IV | 3-0-0-3 | MAT201 |
| IV | CSE301 | Machine Learning | 3-0-0-3 | CSE201 |
| IV | CSE302 | Software Engineering | 3-0-0-3 | CSE201 |
| IV | CSE303 | Web Technologies | 3-0-0-3 | CSE201 |
| V | CSE401 | Advanced Algorithms | 3-0-0-3 | CSE301 |
| V | CSE402 | Embedded Systems | 3-0-0-3 | CSE301 |
| V | CSE403 | Cloud Computing | 3-0-0-3 | CSE302 |
| VI | CSE501 | Big Data Analytics | 3-0-0-3 | CSE401 |
| VI | CSE502 | Internet of Things (IoT) | 3-0-0-3 | CSE401 |
| VI | CSE503 | Research Methodology | 2-0-0-2 | - |
| VII | CSE601 | Capstone Project I | 4-0-0-4 | CSE501 |
| VIII | CSE602 | Capstone Project II | 4-0-0-4 | CSE601 |
The departmental elective courses offer students the opportunity to delve deeper into specialized areas of interest. Below are descriptions of ten advanced departmental electives:
- Deep Learning and Neural Networks: This course explores deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students implement models for image classification, natural language processing, and generative AI using frameworks like TensorFlow and PyTorch.
- Cryptography and Network Security: Designed to equip students with knowledge of cryptographic algorithms, secure protocols, and network defense strategies. Practical sessions include implementing firewalls, intrusion detection systems, and penetration testing tools.
- DevOps & Cloud Infrastructure: Covers CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and cloud platforms like AWS, Azure, and GCP. Students deploy scalable applications using industry-standard tools and practices.
- Robotics and Automation: Combines mechanical engineering principles with software control systems to build robots capable of autonomous navigation, manipulation, and human interaction. Emphasis on sensor fusion, real-time systems, and simulation environments.
- Computer Vision and Image Processing: Focuses on algorithms for object detection, segmentation, and recognition using computer vision libraries like OpenCV and scikit-image. Applications include facial recognition, medical imaging, and autonomous vehicles.
- Quantum Computing Fundamentals: Introduces quantum bits (qubits), superposition, entanglement, and quantum algorithms. Includes simulation exercises using Qiskit and Cirq frameworks to understand potential applications in optimization and cryptography.
- Reinforcement Learning: Explores how agents learn optimal actions through interaction with environments. Students train agents for games, robotics control, and decision-making systems using libraries like Stable Baselines3 and Ray RLlib.
- Big Data Engineering: Covers Hadoop ecosystem, Spark frameworks, NoSQL databases, and streaming analytics. Practical labs involve designing distributed data pipelines for processing large-scale datasets.
- Human-Computer Interaction: Analyzes user experience design, usability testing, and interface prototyping. Students develop interactive applications using design thinking methodologies and tools like Figma and Adobe XD.
- Mobile Application Development: Focuses on native and cross-platform mobile app development using React Native, Flutter, and Kotlin/Java. Students build apps for iOS and Android with features such as push notifications, location services, and offline functionality.
The department strongly emphasizes project-based learning as a core component of the B.Tech experience. From the second year onwards, students engage in mini-projects that help them apply theoretical knowledge to real-world challenges. These projects are typically completed in teams under faculty guidance, allowing students to develop collaboration skills and technical proficiency.
For the final-year capstone project, students select a research topic aligned with their specialization or industry needs. They work closely with a faculty mentor who provides supervision throughout the process. The evaluation criteria include innovation, implementation quality, documentation, presentation, and peer review. Projects often lead to publications in conferences or journals, and some even result in patents or startup ideas.
Mini-Projects & Final-Year Thesis Structure
Mini-projects begin in the third year and last for two semesters. Each project is assigned a faculty member who acts as a mentor and evaluator. The selection process involves submitting proposals, which are reviewed by the departmental committee based on feasibility, relevance, and innovation potential.
The final-year thesis/capstone project requires students to conduct original research or develop a significant application. The timeline spans a full academic year, with milestones at mid-term and end-of-year presentations. Evaluation includes a written report, oral defense, and demonstration of the deliverable product or solution.