In this report, F# contributor Tomas Petricek explains many of the key features of the F# language that make it a great tool for data science and machine learning. Real world examples take you through the entire data science workflow with F#, from data access and analysis to presenting the results. You'll learn about:

How F# and its unique features—such as type providers—ease the chore of data access

The process of data analysis and visualization, using the Deedle library, R type provider and the XPlot charting library

Implementations for a clustering algorithm using the standard F# library and how the F# type inference helps you understand your code

The report also includes a list of resources to help you learn more about using F# for data science.