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Software Design and Architecture BSCS (CS-4008)

Course, Riphah International University, CS Department, 2017

Course Description

This course provides students with a comprehensive understanding of the principles and practices essential for designing and architecting robust, scalable, and maintainable software systems.

Cloud Computing BSCS (CS-4020)

Course, Fast University, CS Department, 2024

Course Description

This course provides students with an in-depth understanding of cloud computing concepts, architectures, and services. It covers the design and deployment of cloud-based solutions, emphasizing scalability, cost-efficiency, and performance optimization.

Data Mining MSCS (CS-4012 )

Course, Fast University, CS Department, 2024

Course Description

This course introduces students to the concepts, techniques, and tools essential for discovering patterns and knowledge from large datasets. It emphasizes practical applications in real-world scenarios and provides insights into the data-driven decision-making process.

Software Design and Analysis BSCS (CS-4015 )

Course, Fast University, CS Department, 2024

Course Description

This course focuses on the principles of software design and analysis, emphasizing the use of Unified Modeling Language (UML) and design patterns for building high-quality software systems. It combines theoretical concepts with practical implementation in Java.

DevOps & Cloud Computing MSCS (CS-5021)

Course, Fast University, CS Department, 2024

Course Description

This advanced course delves into the practices of DevOps and the architecture of cloud computing, with a dedicated focus on Amazon Web Services (AWS). It equips students with the skills to design, deploy, and manage scalable and resilient applications using AWS tools and DevOps methodologies.

Machine Learning BSCS (CS-4090)

Course, Fast University, CS Department, 2025

Course Description

This basic course explores the concepts, techniques, and applications of Machine Learning (ML), providing students with the theoretical foundations and hands-on skills required to design, train, and evaluate predictive models. Students will work with Python-based ML tools and libraries such as Scikit-learn, NumPy, and Pandas to solve real-world problems.