The Galyleo Analytics Platform represents a significant advancement in data analytics, building upon the strong foundation of JupyterHub. It integrates a wide array of popular analytics libraries, providing users with a comprehensive suite of tools for data analysis. With the inclusion of RStudio, Shiny and VSCode, it offers a versatile environment that caters to various programming needs, from R to Python, ensuring a seamless experience for data scientists and analysts. This platform exemplifies the evolution of open-source tools in facilitating sophisticated data exploration and visualization.

JupyterHub, the key technology underpinning the Galyleo Analytics Platform, offers the most comprehensive suite of analytics tools on Earth. One of these is Jupyter Notebooks, with over 34 million daily users. JupyterHub is used as a campuswide analytics solution at over 30 Universities in North America, including the University of California, Berkeley, the University of Washington, and the University of British Columbia.

• Python, R, and Julia Notebooks included.
• RStudio, Shiny, and VSCode (the open-source version of Microsoft's popular Visual Studio Code environment) available in selected environments
• Integrated Linux command terminal integrated in every environment.
• Integrated user storage in every environment, shared across environments.
• Graphics Processing Units (GPUs) available on selected environments.
• Hardware resources are settable on a per-environment basis.
• Customized environments with specific libraries, executables, or integrated applications can be designed and deployed

A Galyleo Analytics Platform Environment is a suite of libraries (Python, R, and other), Linux applications, hardware specifications and web-presented applications (e.g., Shiny, VSCode, RStudio). Currently supported environments are:

• Scientific Computing: Designed for science simulations, this environment contains Julia as well as the Python scientific computing libraries/li>
• Data Science: designed for data science and analytics, this includes popular data science tools and libraries, including PANDAS and RStudio
• Quantum Computing: Designed for developing and testing quantum computing codes, this environment contains Qiskit
• Machine Learning: this environment contains popular ML tools including Pytorch
• Parallel Computing: this environment includes tools for parallel analytics, including Apache Spark, DASK, and Apache Toree