Getting Started with Haskell Data Analysis

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  • 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

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.

  • 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
Page Count 160
Course Length 4 hours 48 minutes
ISBN 9781789802863
Date Of Publication 31 Oct 2018


James Church

James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.