Analyzing and Visualizing Data with F#

Chapter 4. Conclusions and Next Steps

This brief report shows just a few examples of what you can do with F#, but we used it to demonstrate many of the key features of the language that make it a great tool for data science and machine learning. With type providers, you can elegantly access data. We used the XPlot library for visualization, but F# also gives you access to the ggplot2 package from R and numerous other tools. As for analysis, we used the Deedle library and R type provider, but we also implemented our own clustering algorithm.

Adding F# to Your Project

I hope this report piqued your interest in F# and showed some good reasons why you might want to use it in your projects. So, what are the best first steps? First of all, you probably should not immediately switch all your code to F# and become the only person in your company who understands it!

A large part of any machine learning and data science is experimentation. Even if your final implementation needs to be in C# (or any other language), you can still use F# to explore the data and prototype different algorithms (using plain F#, R type provider, and the machine learning libraries discussed below).

F# integrates well with .NET and Xamarin applications, so you can write your data access code or a machine learning algorithm in F# and use it in a larger C# application. There are also many libraries for wrapping F# code as a web application or a web service;¹ and so you can expose the functionality as a simple REST ...