The report is freely available from O'Reilly. To get it, follow the link below. This page provides more information, errata and also the latest version of the source code.
The examples in this chapter focus on the access part of the data science workflow. In most languages, this is typically the most frustrating part of the access, analyze, visualize loop. In F#, type providers come to the rescue!
In this chapter, we look at a more realistic case study of doing data science with F#. We use World Bank as our data source, but this time we call it directly using the XML provider. This demonstrates a general approach that works with any REST-based service.
This chapter completes our brief tour by using the F# language to implement the k-means clustering algorithm. This illustrates two aspects of F# that make it nice for writing algorithms: type inference and interactive development style.
If you want to learn more about using F# for data science and machine learning, there are a number of excellent resources that are worth checking out now that you have finished the quick overview from this report. This chapter gives you a good list!
Change in CO2 emissions between 2000 and 2010 across the world (Chapter 2).
Is life expectancy correlated with (a logarithm of the) GDP? (Chapter 2).
Looking at correlations between indicators about the world (Chapter 2).
Automatically clustering countries in the world based on growth indicators (Chapter 3).