Clojure Data Analysis Cookbook

Over 110 recipes to help you dive into the world of practical data analysis using Clojure

Overview

• Get a handle on the torrent of data the modern Internet has created
• Recipes for every stage from collection to analysis
• A practical approach to analyzing data to help you make informed decisions

In Detail

Data is everywhere and it’s increasingly important to be able to gain insights that we can act on. Using Clojure for data analysis and collection, this book will show you how to gain fresh insights and perspectives from your data with an essential collection of practical, structured recipes.

“The Clojure Data Analysis Cookbook” presents recipes for every stage of the data analysis process. Whether scraping data off a web page, performing data mining, or creating graphs for the web, this book has something for the task at hand.

You’ll learn how to acquire data, clean it up, and transform it into useful graphs which can then be analyzed and published to the Internet. Coverage includes advanced topics like processing data concurrently, applying powerful statistical techniques like Bayesian modelling, and even data mining algorithms such as K-means clustering, neural networks, and association rules.

What you will learn from this book

• Create beautiful, insightful graphs that you can publish to the Internet.
• Apply powerful clustering and data mining techniques to better understand your data.
• Use powerful data analysis libraries like Incanter, Hadoop, and Weka to get things done quickly.
• Interface with Mathematica and R to use the powerful analysis features they provide.
• Process data concurrently and in parallel for faster performance.
• Transform data to make it more useful and easier to analyze.

Approach

Full of practical tips, the “Clojure Data Analysis Cookbook” will help you fully utilize your data through a series of step-by-step, real world recipes covering every aspect of data analysis.

Who this book is written for

Prior experience with Clojure and data analysis techniques and workflows will be beneficial, but not essential.

Table of Contents

Chapter 1: Importing Data for Analysis
Chapter 2: Cleaning and Validating Data
Chapter 3: Managing Complexity with Concurrent Programming
Chapter 4: Improving Performance with Parallel Programming
Chapter 5: Distributed Data Processing with Cascalog
Chapter 6: Working with Incanter Datasets
Chapter 7: Preparing for and Performing Statistical Data Analysis with Incanter
Chapter 8: Working with Mathematica and R
Chapter 9: Clustering, Classifying, and Working with Weka
Chapter 10: Graphing in Incanter
Chapter 11: Creating Charts for the Web