Course Structure Overview

The Computer Engineering program at SHA SHIB COLLEGE OF TECHNOLOGY 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 a strong foundation in both theoretical concepts and practical applications, preparing them for diverse career paths in the technology industry.

First Year

Second Year

Third Year

Fourth Year

Departmental Electives (Third Year)

• Advanced Data Structures: Focuses on advanced data structures and algorithms, covering topics like trees, graphs, hash tables, and dynamic programming. This course builds upon the foundational knowledge gained in Data Structures and Algorithms.
• Compiler Design: Explores the design and implementation of compilers, including lexical analysis, parsing, semantic analysis, code generation, and optimization techniques.
• Distributed Systems: Covers principles of distributed computing, including distributed algorithms, consensus protocols, fault tolerance, and cloud computing models.
• Computer Graphics: Introduces fundamental concepts in computer graphics, including rendering pipelines, transformations, lighting models, and animation techniques.
• Image Processing: Provides insights into image processing techniques using mathematical methods, filters, transforms, and applications in medical imaging, satellite imagery, and multimedia systems.

Departmental Electives (Fourth Year)

• Natural Language Processing: Focuses on understanding and generating human language through computational methods, including syntactic parsing, semantic analysis, and neural language models.
• Computer Vision: Covers techniques for extracting meaningful information from visual data, including object detection, recognition, segmentation, and 3D reconstruction.
• Cloud Computing: Explores cloud architectures, virtualization technologies, service models (IaaS, PaaS, SaaS), and deployment strategies for scalable applications.
• Mobile App Development: Teaches students how to develop cross-platform mobile applications using frameworks like React Native or Flutter, covering UI/UX design and backend integration.
• Big Data Analytics: Introduces tools and techniques for processing large datasets, including Hadoop, Spark, MapReduce, and machine learning algorithms applied to big data problems.

Project-Based Learning Philosophy

The department believes in integrating project-based learning throughout the curriculum to ensure that students gain practical experience alongside theoretical knowledge. The philosophy centers around experiential learning, where students work on real-world projects that simulate actual industry challenges.

Mini Projects

Mini projects are undertaken during the second and third years, with each project lasting approximately two months. These projects aim to reinforce concepts learned in core courses and develop problem-solving skills. Students form teams of 3-5 members and select a topic from a list provided by faculty mentors or propose their own idea after approval.

Final Year Thesis/Capstone Project

The final year thesis is a significant component of the program, lasting for six months. Students work under the supervision of a faculty mentor on an advanced research or development project. Projects are selected based on student interests and alignment with faculty expertise. The process includes proposal writing, literature review, implementation, testing, documentation, and presentation.

Project Selection Process

Students can choose from a curated list of projects provided by faculty mentors or submit their own proposals for evaluation. The selection criteria include relevance to current industry trends, feasibility within the given timeframe, and alignment with the student's interests and career goals. Faculty members guide students throughout the process, ensuring that each project meets academic standards and provides valuable learning outcomes.