Book Image

Getting Started with Haskell Data Analysis

By : James Church
Book Image

Getting Started with Haskell Data Analysis

By: James Church

Overview of this book

Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner. This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world. By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.
Table of Contents (8 chapters)

Summary

We began the section with installing the gnuplot and the EasyPlot Haskell library. We discussed the moving average using the 200-day and the 50-day moving average, which is frequently used by analysts in market analysis. But beware, these are not substitutions for good, solid research. We discussed how to make our plots publication-ready by adding a legend and saving our images to files, and we discussed how to feature scale our plot so that it's on the range of -1 to 1. Feature scaling always retains the shape of the data.

Finally, we demonstrated the utility of taking the log of data. It allowed us to compress higher values, while retaining the general shape of lower values. This changes the overall shape of a plot. In our next chapter, we will discuss the normal distribution.