Course Structure Overview
The Computer Science and Engineering program at ABES College is structured over eight semesters, with a balanced mix of core courses, departmental electives, science electives, and laboratory sessions. The curriculum is designed to provide students with both breadth and depth in computer science concepts while aligning with industry expectations.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1st | MTH101 | Mathematics I | 3-1-0-4 | None |
| 1st | PHY101 | Physics | 3-1-0-4 | None |
| 1st | CHM101 | Chemistry | 3-1-0-4 | None |
| 1st | ENG101 | English Communication Skills | 2-0-0-2 | None |
| 1st | CSE101 | Computer Programming Using C | 3-0-2-4 | None |
| 1st | ENG102 | Engineering Graphics | 2-0-0-2 | None |
| 1st | CSE102 | Introduction to Computer Science and Engineering | 3-0-0-3 | None |
| 2nd | MTH102 | Mathematics II | 3-1-0-4 | MTH101 |
| 2nd | CSE201 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 2nd | CSE202 | Object-Oriented Programming (OOP) using C++ | 3-0-2-4 | CSE101 |
| 2nd | CSE203 | Database Management Systems (DBMS) | 3-1-0-4 | CSE201 |
| 2nd | CSE204 | Computer Organization and Architecture | 3-1-0-4 | CSE101 |
| 2nd | CSE205 | Operating Systems | 3-1-0-4 | CSE204 |
| 2nd | MTH201 | Probability and Statistics | 3-1-0-4 | MTH101 |
| 3rd | CSE301 | Computer Networks | 3-1-0-4 | CSE205 |
| 3rd | CSE302 | Software Engineering | 3-1-0-4 | CSE202 |
| 3rd | CSE303 | Compiler Design | 3-1-0-4 | CSE201 |
| 3rd | CSE304 | Microprocessor and Interfacing | 3-1-0-4 | CSE204 |
| 3rd | CSE305 | Web Technologies | 3-1-0-4 | CSE202 |
| 3rd | CSE306 | Linear Algebra and Numerical Methods | 3-1-0-4 | MTH102 |
| 4th | CSE401 | Artificial Intelligence | 3-1-0-4 | MTH201 |
| 4th | CSE402 | Cybersecurity | 3-1-0-4 | CSE301 |
| 4th | CSE403 | Data Mining and Machine Learning | 3-1-0-4 | MTH201 |
| 4th | CSE404 | Cloud Computing | 3-1-0-4 | CSE301 |
| 4th | CSE405 | Internet of Things (IoT) | 3-1-0-4 | CSE204 |
| 4th | CSE406 | Digital Image Processing | 3-1-0-4 | CSE201 |
| 5th | CSE501 | Advanced Computer Networks | 3-1-0-4 | CSE301 |
| 5th | CSE502 | Software Testing and Quality Assurance | 3-1-0-4 | CSE302 |
| 5th | CSE503 | Reinforcement Learning | 3-1-0-4 | CSE403 |
| 5th | CSE504 | Big Data Analytics | 3-1-0-4 | CSE403 |
| 5th | CSE505 | Mobile Application Development | 3-1-0-4 | CSE202 |
| 5th | CSE506 | Embedded Systems Design | 3-1-0-4 | CSE304 |
| 6th | CSE601 | Computer Vision | 3-1-0-4 | CSE406 |
| 6th | CSE602 | Natural Language Processing | 3-1-0-4 | CSE403 |
| 6th | CSE603 | DevOps and CI/CD | 3-1-0-4 | CSE302 |
| 6th | CSE604 | Quantum Computing Fundamentals | 3-1-0-4 | MTH201 |
| 6th | CSE605 | Human-Computer Interaction | 3-1-0-4 | CSE202 |
| 6th | CSE606 | Blockchain Technologies | 3-1-0-4 | CSE205 |
| 7th | CSE701 | Capstone Project - Phase I | 3-0-6-6 | None |
| 8th | CSE801 | Capstone Project - Phase II | 3-0-6-6 | CSE701 |
Advanced Departmental Electives
The department offers a wide array of advanced departmental electives that allow students to specialize in emerging fields and explore niche areas within computer science. Below are detailed descriptions of several key courses:
- Deep Learning: This course delves into neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Students will gain hands-on experience with frameworks like TensorFlow and PyTorch.
- Natural Language Processing: Focused on building systems that understand and generate human language, this course covers tokenization, parsing, named entity recognition, sentiment analysis, and machine translation using state-of-the-art models like BERT and GPT.
- Cybersecurity and Ethical Hacking: This course explores network security protocols, cryptography, penetration testing, malware analysis, and digital forensics. Students will learn to defend against cyber threats using real-world tools and scenarios.
- DevOps and CI/CD: Designed for students interested in software development lifecycle management, this course introduces automation tools like Jenkins, Docker, Kubernetes, and GitLab CI/CD pipelines.
- Big Data Analytics: This course covers data processing frameworks such as Apache Hadoop and Spark, along with visualization techniques using tools like Tableau and Power BI. Students will work on large-scale datasets to derive actionable insights.
- Mobile Application Development: Using cross-platform frameworks like React Native and Flutter, students will develop applications for iOS and Android platforms while learning about app store submission processes and user experience design principles.
- Computer Vision: This course focuses on image processing, object detection, facial recognition, and scene understanding using deep learning models. Practical labs involve working with datasets like COCO and ImageNet.
- Quantum Computing Fundamentals: Introducing students to the principles of quantum mechanics and quantum algorithms, this course covers qubits, superposition, entanglement, and quantum error correction using simulators like Qiskit and Cirq.
- Blockchain Technologies: Exploring distributed ledger systems, smart contracts, consensus mechanisms, and decentralized applications (dApps), this course includes hands-on development using Ethereum and Solidity.
- Human-Computer Interaction: This course covers usability testing, user research, interaction design, prototyping, and accessibility standards. Students will conduct field studies and build interactive prototypes for real-world applications.
Project-Based Learning Philosophy
The department strongly emphasizes project-based learning as a core component of the curriculum. This approach ensures that students gain practical experience while applying theoretical knowledge to solve real-world problems. Projects are designed to foster collaboration, creativity, and critical thinking.
Mini-Projects (First Year)
In the first year, students work on mini-projects under faculty supervision. These projects typically last two months and involve solving a small-scale problem using programming languages like Python or C++. Students learn to plan, execute, document, and present their findings in a professional setting.
Final-Year Thesis/Capstone Project
The capstone project is the culmination of the undergraduate experience. Students select a topic aligned with their interests and career goals, often inspired by industry challenges or research opportunities. They form teams, conduct literature reviews, design experiments, implement solutions, and present their work to a panel of faculty members and external experts.
Selection Process
Students are encouraged to choose projects based on their passions and career aspirations. Faculty mentors guide students in selecting relevant topics, ensuring alignment with current industry trends and academic rigor. The selection process involves proposal presentations, timeline planning, and regular progress evaluations.
Evaluation Criteria
The final project is evaluated based on several criteria including technical depth, innovation, documentation quality, presentation skills, and impact potential. Students must submit a detailed report, maintain a logbook, and deliver a live demonstration to the faculty panel.