توصيف مقررات الخطة المحدثة برنامج بكالوريوس تقنية المعلومات 1445هـ, 2023-2024م (المقررات التخصصية)

An advanced cloud computing course is designed to teach students the latest cloud computing technologies and concepts. This course typically covers topics such as cloud computing architecture, cloud infrastructure and virtualization, cloud computing security, cloud data storage, cloud computing frameworks, and cloud migration strategies and best practices. In addition to these core topics, advanced cloud computing courses may also cover other emerging topics suggested by the instructor. Practical case studies will be given to the students in the practical part of the course. Cloud Computing is a prerequisite course for this course. 

Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This course focuses both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, this course covers the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop, MapReduce, Apache, Spark and Flink. The Pre-requisite of this course is introduction to data science.

This course covers the technology and the market around the Internet of Things (IoT), applications, trends and implications. The course also describes the technology used to build these kinds of devices, explain what embedded systems are and how they are used for IoT, how IoT devices communicate, how they store data, and the kinds of distributed systems needed to support them. Student also will be equipped with designing an IoT device to work with a Cloud Computing infrastructure taking security issues into account.

The cloud computing and big data course provides a comprehensive exploration of cloud computing and big data technologies, focusing on their fundamental concepts, architecture, and practical implementation. Students will learn how to design, deploy, and manage cloud-based big data solutions to harness the power of scalable computing and data analytics. The course covers various aspects, including cloud computing models, virtualization, storage and networking in the cloud, big data processing frameworks, analytics techniques, and data management. Practical hands-on exercises and projects will enable students to gain practical experience in working with cloud and big data technologies. 

This course introduces the fundamentals of machine learning (ML) and its techniques. It enables students to use ML techniques to build real-world AI applications. It covers the main ML approaches and algorithms including supervised learning (regression and classification), unsupervised learning (dimensionality reduction and clustering), and model evaluating metrics. By the end of this course, students will have master key concepts and gain the practical know-how to quickly and powerfully apply machine learning techniques to solve real-world problems. The course teaching strategy is based on a combination of active learning and practical application. The pre-requisite of this course is introduction to data science.

This course is all about data visualization, the art and science of turning data into readable graphics. It explores how to design and create data visualizations based on data available and tasks to be achieved. Topics covered include overview of data visualization, the shapes of data, marks and channels, arrange tables, visualization of spatial data, networks, and trees, using color and size in visualization, manipulate view & facet into multiple views, and data reduction. Students will learn to evaluate the effectiveness of visualization designs, and think critically about each design decision, such as choice of color and choice of visual encoding. Students will also learn how to create their own data visualizations and learn to use Open-Source data visualization tools. The Pre-requisite of this course is introduction to data science.

This course introduces the concepts and techniques of deep learning (DL). It teaches students how to build and train neural network architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, and more. Acquire students with the knowledge and skills required to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as chatbots, natural language processing, and more. The course teaching strategy is based on a combination of active learning and practical application. The pre-requisite of this course is introduction to data science.