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Getting Started with Haskell Data Analysis: Put your data analysis techniques to work and generate publication-ready visualizations
Authors: James Church
ISBN-10 书号: 1789802865
ISBN-13 书号: 9781789802863
Publisher Finelybook 出版日期: 2018-10-31
pages 页数: 160 pages
Book 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.
Contents
1: DESCRIPTIVE STATISTICS
2: SQLITE3
3: REGULAR EXPRESSIONS
4: VISUALIZATIONS
5: KERNEL DENSITY ESTIMATION
6: COURSE REVIEW
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
Authors
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.
