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.

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Table of Contents (8 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Descriptive Statistics
Descriptive Statistics
The CSV library – working with CSV files
Data range
Data mean and standard deviation
Data median
Data mode
Summary
2
SQLite3
SQLite3
SQLite3 command line
Working with SQLite3 and Haskell
Slices of data
Working with SQLite3 and descriptive statistics
Summary
3
Regular Expressions
Regular Expressions
Dots and pipes
Atom and Atom modifiers
Character classes
Regular expressions in CSV files
SQLite3 and regular expressions
Summary
4
Visualizations
Visualizations
Line plots of a single variable
Plotting a moving average
Creating publication-ready plots
Feature scaling
Scatter plots
Summary
5
Kernel Density Estimation
Kernel Density Estimation
The central limit theorem
Normal distribution
Introducing kernel density estimation
Application of the KDE
Summary
6
Course Review
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
7
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Course Review

In this chapter, we're going to tie together the first five chapters, using the MovieLens dataset from the University of Minnesota. We're going to cover the highlights from all of the chapters, and hopefully drop in some new content along the way. So, in this chapter we are going to cover the following topics:

  • Converting CSV variation files into SQLite3
  • Using SQLite3 SELECT and the DescriptiveStats module for descriptive statistics
  • Creating compelling visualizations using EasyPlot
  • Applying the kernel density estimator to the MovieLens dataset
Previous Section
End of Section 1
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