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Getting Started with Haskell Data Analysis

Getting Started with Haskell Data Analysis

By : Church
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Getting Started with Haskell Data Analysis

Getting Started with Haskell Data Analysis

By: 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.
Table of Contents (8 chapters)
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Kernel Density Estimation

In this chapter, we are going to cover kernel density estimation. Since kernel density estimation is a rather involved operation, we're going to split this up over several sections so hopefully it's easier to digest.

In this chapter, we are going to cover the following topics:

  • Introducing the central limit theorem
  • Introducing normal distribution
  • Introducing the kernel density estimator
  • Applying the kernel density estimator to a dataset

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