Learning Haskell Data Analysis

Want to learn data analysis? Then learn Haskell with this practical data science tutorial, designed to help you dive into data with the functional language
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Learning Haskell Data Analysis

James Church

Want to learn data analysis? Then learn Haskell with this practical data science tutorial, designed to help you dive into data with the functional language
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Book Details

ISBN 139781784394707
Paperback198 pages

Book Description

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.

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: Hypothesis Testing
Data in a coin
Does a home-field advantage really exist?
Summary
Chapter 6: 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
Chapter 7: Naive Bayes Classification of Twitter Data
An introduction to Naive Bayes classification
Creating a Twitter application
Summary
Chapter 8: 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

What You Will Learn

  • Learn the essential tools of Haskell needed to handle large data
  • Migrate your data to a database and learn to interact with your data quickly
  • Clean data with the power of Regular Expressions
  • Plot data with the Gnuplot tool and the EasyPlot library
  • Formulate a hypothesis test to evaluate the significance of your data
  • Evaluate the variance between columns of data using a correlation statistic and perform regression analysis

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: Hypothesis Testing
Data in a coin
Does a home-field advantage really exist?
Summary
Chapter 6: 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
Chapter 7: Naive Bayes Classification of Twitter Data
An introduction to Naive Bayes classification
Creating a Twitter application
Summary
Chapter 8: 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

Book Details

ISBN 139781784394707
Paperback198 pages
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