James Church - Getting Started with Haskell Data Analysis

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James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.

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Data analysis is part computer science and part statistics. An important part of data analysis is validating your assumptions with real-world data to see whether there is a pattern, or a particular user behavior that you can validate.

In this book, we are going to learn about data analysis from the perspective of the Haskell programming language. The goal of this book is to take you from being a beginner in math and statistics, to the point that you feel comfortable working with large-scale datasets. While mathematics is a big part of data analysis, we've tried to keep this book simple and approachable so that you can apply what you learn to the real world.

This book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into a functional programming style.

, Descriptive Statistics , teaches you about the Text.CSV library. It also covers some of the descriptive statistics functions, such as mean, median, and mode.

, SQLite3 , focuses on how to get the data from CSV into SQLite3. You will understand the data types of SQLite3 and how to fetch data using SQL statements. It also covers how to create your own custom module of descriptive statistics.

, Regular Expressions , introduces you to regular expression syntax, such as dots and pipe. It also covers character classes at length. Finally, it teaches you how to use regular expressions within a CSV file and an SQLite3 database.

, Visualizations , starts with the installation of gnuplot and the EasyPlot Haskell library. It covers how to use moving average function to analyze stock data. Finally, it teaches you how to make publication-ready plots by adding legends and saving those plots to files.

, Kernel Density Estimation , introduces you to central limit theorem and normal distribution and helps you to understand the difference between them. Later, it talks about the kernel density estimator and how to apply it to a dataset.

, Course review , works on the MovieLens data by applying what you have learned from the first five chapters. In addition to what was covered in the earlier chapters, you will also be exploring a few more interesting techniques for analyzing the data.

You will need to set up the IHaskell notebook environment to test the examples in these chapters. You will also need some knowledge of the Haskell programming language, math, and statistics.

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