This library of complimentary course content is intended for use in classroom settings. Modules are divided by scenario (Cloud Computing, Web Dev, Data Science, DevOps) and can function autonomously or can be taught progressively as a course. Most modules includes presentations, speaker notes, and labs that help students demonstrate knowledge of the material. Your feedback is appreciated as we continue to improve this library.
If you want to report any issues we need to fix. Please log an issue. Include the content section (Tech Talk, Workshop, Course Content), module number and title, along with any error messages and screenshots.
| Module | Title | Description | Labs link |
|---|---|---|---|
| 1 | Introduction To Cloud Computing | This module introduces what cloud computing is, why it has become so popular, and the existing approaches to cloud computing. The module 1 labs cover real cloud computing examples. Upon completion of this module, the students should understand the fundamental concepts of cloud computing and how to apply them. | Labs |
| 2 | Web Development | This module introduces the foundations of JavaScript — the language of the web. Additionally this module covers using Node.js to build a back-end, how to use JavaScript on a server to build RESTful APIs and how to deploy to a scalable and convenient cloud hosting platform (Azure Cloud Services/Websites) with persistent data using a NoSQL database (Azure Storage). | Labs |
| 3 | Cross-Platform Mobile Application Development using Xamarin | This module introduces a survey of mobile development, the basics of Xamarin, and the foundations of C#. This module proceeds to cover Xamarin development basics for Android, iOS, and Windows apps; including the basics of using Xamarin.Forms and testing Xamarin apps. Finally, this module demonstrates deploying a Cross-platform mobile app (using Azure app services) and getting the app into an app store. | Labs |
| 4 | Data Analysis Using Hadoop | This module introduces Big Data, its platforms, and its analysis. Hadoop is the most popular approach to Big Data, for storing and computing statistics for massive data sets. The module describes Big Data, Hadoop, Hadoop's ecosystems, and Hive. In the labs, Hive is presented as the main context to show how massive data sets can be analyzed. At the end of this module, students should know how to use Hive for Big Data Analysis. | Labs |
| 5 | Data Science and Machine Learning in Spark | This module introduces the fundamental concepts of data science and machine learning using Spark and Spark Machine Learning library. Thus, at the end of the course, students should know the fundamental concepts of machine learning and be adapt Spark for machine learning and data science to predict the trend and patterns of massive data sets. | Labs |
| 6 | Internet of Things (IoT) | This module covers the fundamental concepts of the Internet of Things. IoT can be considered a system of sensor devices and data sources that generate streaming data. Thus, at the end of the course, students should know how to collect streaming data from IoT devices and how to adopt and analyze the streaming data from these devices. | Labs |
| 7 | DevOps | This module covers the fundamental concepts of DevOps including configuration management. This module introduces Chef. Additionally, module 7 teaches how to launch a web server, desired state configurations, and how to test a web server (all using Chef). | Labs |