Chapter 1, Tools of the Trade, discusses the software and the essential libraries used in the book. We will also solve two simple problems—how to find the median of a list of numbers and how to locate the vowels in a word. These problems serve as an introduction to working with small datasets. We also suggest two nonessential tools to assist you with the projects in this text—Git and Tmux.

Chapter 2, Getting Our Feet Wet, introduces you to csv files and SQLite3. CSV files are human- and machine-readable and are found throughout the Internet as a common format to share data. Unfortunately, they are difficult to work with in Haskell. We will introduce a module to convert csv files into SQLite3 databases, which are comparatively much easier to work with. We will obtain a small csv file from the US Geological Survey, convert this dataset to an SQLite3 database, and perform some analysis on the earthquake data.

Chapter 3, Cleaning Our Datasets, discusses the oh-so-boring, yet oh-so-necessary topic of data cleaning. We shouldn't take clean, polished datasets for granted. Time and energy must be spent on creating a metadata document for a dataset. An equal amount of time must also be spent cleaning this document. This involves looking for blank entries or entries that do not fit the standard that we defined in our metadata document. Most of the work in this area is performed with the help of regular expressions. Regular expressions are a powerful tool by which we can search and manipulate data.

Chapter 4, Plotting, looks at the plotting of data. It's often easier to comprehend a dataset visually than through raw numbers. Here, we will download the history of the publicly traded companies on the New York Stock Exchange and discuss the investment strategy of growth investing. To do this, we will visually compare the yearly growth rate of Google, Microsoft, and Apple. These three companies belong to a similar industry (technology) but have different growth rates. We will discuss the normalization function, which allows us to compare companies with different share prices on the same graph.

Chapter 5, Hypothesis Testing, trains us to be skeptical of our own claims so that we don't fall for the trap of fooling ourselves. We will give ourselves the challenge of detecting an unfair coin. Successive coin flips follow a particular pattern called the binomial distribution. We will discuss the mathematics behind detecting whether a particular coin is following this distribution or not. We will follow this up with a question about baseball—"Is there a benefit if one has home field advantage?" To answer this question, we will download baseball data and put this hypothesis to the test.

Chapter 6, Correlation and Regression Analysis, discusses regression analysis. Regression analysis is a tool by which we can interpolate data where there is none. In keeping with the baseball theme, we will try to measure how much benefit there is to scoring baseball runs and winning baseball games. We will compute the runs-per-game and the win percentage of every team in Major League Baseball for the 2014 season and evaluate who is overperforming and underperforming on the field. This technique is simple enough to be used on other sports teams for similar analysis.

Chapter 7, Naive Bayes Classification of Twitter Data, analyzes the tweets from the popular social networking site, Twitter. Twitter has broad international appeal and people from around the world use the site. Twitter's API allows us to look at the language of each tweet. Using the individual words and the identified language, we will build a Naive Bayes classifier to detect the language of the sentences based on a database of downloaded tweets.

Chapter 8, Building a Recommendation Engine, continues with the analysis of the Twitter data and helps us create our own recommendation engine. This recommendation will help users find other users with similar interests based on the frequency of the words used in their tweets. There is a lot of data in word frequencies and we don't need all of it. So, we will discuss a technique to reduce the dimensionality of our data called Principal Component Analysis (PCA). PCA engines are used to recommend similar products for you to purchase or watch movies on commercial websites. We will cover the math and the implementation of a recommendation engine from scratch.

In each chapter we will introduce new functions. These functions will be added to a module file titled LearningDataAnalysis0X (where X is the current chapter number). We will frequently use functions from the earlier chapters to solve the problem from the chapter at hand. It will help you follow the chapters of this book in order so that you know when special functions mentioned in this book have been introduced.

Appendix, Regular Expressions in Haskell, focuses on the use of regular expressions in Haskell. If you aren't familiar with regular expressions, this will be a short reference guide to their usage.

The software required for this book is the Haskell platform, the cabal tool to install libraries (which comes with Haskell), as well as tools such as SQLite3, gnuplot, and the LAPACK library for linear algebra. The installation instructions for each piece of software are mentioned at the time when the software is needed.

We tried to be cross-platform in this book because Haskell is a cross-platform language. SQLite3 and gnuplot are available for the Windows, Mac, and Linux operating systems. One problem that we encountered while writing this book was the difficulty in installing LAPACK for Windows, which is used in Chapter 8, Building a Recommendation Engine. At the time of writing this book, it is possible to get LAPACK to run on Windows, but the instructions are not that clear and hence it is not recommended. Instead, we recommend Windows users install Debian or Ubuntu Linux using VM software (such as Oracle VirtualBox).

If you are a developer, an analyst, or a data scientist who wants to learn data analysis methods using Haskell and its libraries, then this book is for you. Prior experience with Haskell and basic knowledge of data science will be beneficial.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The percentChange function only computes a single percent change at a given point in our data."

A block of code is set as follows:

module LearningDataAnalysis04 where
import Data.List
import Database.HDBC.Sqlite3
import Database.HDBC
import Graphics.EasyPlot
import LearningDataAnalysis02

Any command-line input or output is written as follows:

sudo apt-get install gnuplot

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "On the Historical Prices page, identify the link that says Download to Spreadsheet."

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