In this chapter, we converted a tab-separated and a pipe separated file into SQLite3. We performed a table join to combine ratings with movie genres because we have datasets that involve multiple tables, and sometimes that requires a table join. We performed a table join followed by a GROUP BY in order to study how movie ratings change with the age of the viewer. Finally, we performed KDE on individual movies and compared the two directly, despite the fact that they have different values in each dataset. So, that is the benefit of the kernel density estimator.
Getting Started with Haskell Data Analysis
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Getting Started with Haskell Data Analysis
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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.
Table of Contents (8 chapters)
Preface
Free Chapter
Descriptive Statistics
SQLite3
Regular Expressions
Visualizations
Kernel Density Estimation
Course Review
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