Getting Started with Haskell Data Analysis: Put your data analysis techniques to work and generate publication-ready visualizations

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SQLite3

In this chapter, we are going to learn about SQLite3. SQLite3 is a file format for storing data in the same mindset of a relational database such as Oracle, MySQL, MariaDB, and Postgres. Well, I suppose that you could use SQLite3 as a traditional database engine, but it's really not made for that. SQLite3 allows us to open up a file, work with the data in that file using SQL statements, and then close that file. One file can contain one database, and a database can store multiple tables of information. Contrast this with the CSV file format that we used in the last chapter which, at best, stores a single table of information. The advantages of SQLite3 are that it doesn't require any server-side programs running in the background, there are no configurations to discuss, and the executable for working with SQLite3 is a single file. So, in my opinion, working with...

Key benefits

Take your data analysis skills to the next level using the power of Haskell

Understand regression analysis, perform multivariate regression, and untangle different cluster varieties

Create publication-ready visualizations of data

Description

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.

What you will learn

Learn to parse a CSV file and read data into the Haskell environment

Create Haskell functions for common descriptive statistics functions

Create an SQLite3 database using an existing CSV file

Learn the versatility of SELECT queries for slicing data into smaller chunks

Apply regular expressions in large-scale datasets using both CSV and SQLite3 files

Create a Kernel Density Estimator visualization using normal distribution

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