Getting Started with Haskell Data Analysis

By : Church

Buy this Book

Getting Started with Haskell Data Analysis

By: Church

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Descriptive Statistics

The CSV library – working with CSV files

Data range

Data mean and standard deviation

Data median

Data mode

Summary

SQLite3

SQLite3 command line

Working with SQLite3 and Haskell

Slices of data

Working with SQLite3 and descriptive statistics

Summary

Regular Expressions

Dots and pipes

Atom and Atom modifiers

Character classes

Regular expressions in CSV files

SQLite3 and regular expressions

Summary

Visualizations

Line plots of a single variable

Plotting a moving average

Creating publication-ready plots

Feature scaling

Scatter plots

Summary

Kernel Density Estimation

The central limit theorem

Normal distribution

Introducing kernel density estimation

Application of the KDE

Summary

Course Review

Converting CSV variation files into SQLite3

Using SQLite3 SELECT and the DescriptiveStats module for descriptive statistics

Creating compelling visualizations using EasyPlot

Reintroducing kernel density estimation

Summary

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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.