This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques.
You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.
Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code.
Who this book is for
This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.