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Clojure Data Analysis Cookbook
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Amazon.com Product Description (ISBN 178216264X, Paperback)
Over 110 recipes to help you dive into the world of practical data analysis using Clojure
OverviewGet 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
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 bookCreate 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.
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.
(retrieved from Amazon Thu, 12 Mar 2015 18:10:14 -0400)
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