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.