Book Image

Learning Haskell Data Analysis

By : James Church
Book Image

Learning Haskell Data Analysis

By: James Church

Overview of this book

<p>Haskell is trending in the field of data science by providing a powerful platform for robust data science practices. This book provides you with the skills to handle large amounts of data, even if that data is in a less than perfect state. Each chapter in the book helps to build a small library of code that will be used to solve a problem for that chapter. The book starts with creating databases out of existing datasets, cleaning that data, and interacting with databases within Haskell in order to produce charts for publications. It then moves towards more theoretical concepts that are fundamental to introductory data analysis, but in a context of a real-world problem with real-world data. As you progress in the book, you will be relying on code from previous chapters in order to help create new solutions quickly. By the end of the book, you will be able to manipulate, find, and analyze large and small sets of data using your own Haskell libraries.</p>

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Learning Haskell Data Analysis
James Church
May, 2015 | 198 pages
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Table of Contents (16 chapters)
Learning Haskell Data Analysis
Learning Haskell Data Analysis
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Tools of the Trade
Tools of the Trade
Welcome to Haskell and data analysis!
Why Haskell?
Getting ready
Nearly essential tools of the trade
Our first Haskell program
Interactive Haskell
Summary
2
Getting Our Feet Wet
Getting Our Feet Wet
Type is king – the implications of strict types in Haskell
Working with csv files
Converting csv files to the SQLite3 format
Summary
3
Cleaning Our Datasets
Cleaning Our Datasets
Structured versus unstructured datasets
Creating your own structured data
Counting the number of fields in each record
Filtering data using regular expressions
Searching fields based on a regular expression
Summary
4
Plotting
Plotting
Plotting data with EasyPlot
Simplifying access to data in SQLite3
Plotting data from a SQLite3 database
Plotting multiple datasets
Plotting a moving average
Summary
5
Hypothesis Testing
Hypothesis Testing
Data in a coin
Does a home-field advantage really exist?
Summary
6
Correlation and Regression Analysis
Correlation and Regression Analysis
The terminology of correlation and regression
Study – is there a connection between scoring and winning?
Regression analysis
The pitfalls of regression analysis
Summary
7
Naive Bayes Classification of Twitter Data
Naive Bayes Classification of Twitter Data
An introduction to Naive Bayes classification
Creating a Twitter application
Summary
8
Building a Recommendation Engine
Building a Recommendation Engine
Analyzing the frequency of words in tweets
Working with multivariate data
Preparing our environment
Performing linear algebra in Haskell
Principal Component Analysis in Haskell
Building a recommendation engine
Summary
Regular Expressions in Haskell
Regular Expressions in Haskell
A crash course in regular expressions
Index
Index

About the Author

James Church is an assistant professor of computer science at the University of West Georgia. James completed his PhD in computer science from the University of Mississippi under the advisement of Dr. Yixin Chen, with a research focus on computational geometry.

While at the University of Mississippi, he learned the skills necessary for data analysis in his side job, where he worked as a database administrator and analyst for the Marijuana Potency Monitoring Program (MPMP) led by Dr. Mahmoud ElSohly. The software written by James is used by the laboratory to store and track the chemical composition of marijuana samples. This data is provided to the United States National Institute on Drug Abuse to report marijuana potency.

The knowledge gained through his experience as an analyst for the MPMP (as well as other companies) was turned into a data analysis course for undergraduates at the University of Mississippi. This course was taught using the languages of Python and R.

James enjoys spending time with his wife, Michelle Allen, teaching, and playing board games with his friends.

I would like to thank the reviewers for testing and reshaping the code in these pages into something readable. While I made every attempt to write the best possible book on this topic, I'm sure that mistakes will be found. All mistakes are my own.

I would like to thank Michelle Allen. Your support made this book possible (Michelle drew the sketch of the airplane found in Chapter 8, Building a Recommendation Engine).

I would like to thank Dr. Yixin Chen, Dr. Dawn Wilkins, Dr. Conrad Cunningham, and the faculty of computer and information science at the University of Mississippi.

Finally, thanks, mom and dad.

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