Book Image

Getting Started with Haskell Data Analysis

By : James Church
Book Image

Getting Started with Haskell Data Analysis

By: James 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)

Introducing kernel density estimation

Kernel density estimation is a process by which we can estimate the shape of a dataset. After we have computed the shape of a dataset, we can compute the probability in which an event will happen.

In this section, we're going to introduce the kernel density estimator. The kernel density estimator requires a kernel function, and we are going to discuss the requirements of the kernel function and how the normal distribution meets those requirements. Finally, we're going to compute the KDE of a set of values. So, kernel density estimation tries to estimate the shape of a dataset. All data has a shape - we could also refer to this as the density - and that shape is not always clear. Once we have estimated the shape of a dataset, we can compute the probability of a particular observation.

We require a kernel function, and in this section...