This chapter introduces cloud computing platforms essential for modern data science work. It covers three major cloud services: Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Students learn to create virtual machines, configure storage, and access cloud resources through SSH connections. The chapter demonstrates hands-on Python development using browser-based IDEs like Google Colab, Azure Machine Learning notebooks, and AWS Cloud9. Key topics include setting up accounts, managing costs through free tiers, and leveraging cloud resources for data science projects. The chapter also covers Hadoop for big data processing and discusses platform migration strategies. Practical exercises guide students through currency conversion programs, interactive calculations, and Olympic year predictions, emphasizing that cloud computing skills are now essential for data science professionals due to scalable processing power and storage capabilities.

This chapter introduces cloud computing platforms essential for modern data science work. It covers the three major providers: Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS).

Key topics include setting up virtual machines, configuring SSH access, and running RStudio Server in browser-based environments on each platform. The chapter demonstrates how to migrate data science workflows from local machines to cloud infrastructure, providing scalable computing resources and storage.

Practical examples show installing R and RStudio on cloud VMs, accessing them through web browsers, and managing costs. The chapter emphasizes that cloud computing skills are now essential for data science practitioners, offering dynamic scaling, redundancy, and pay-as-you-use pricing models for computational resources.