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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Toc

Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Introduction


Over the course of the last few chapters, we've been progressively moving outward. We started with the assumption that everything will run on one processor, probably in a single thread. Then we looked at how to structure our program without this assumption, performing different tasks on many threads. We then tried to speed up processing by getting multiple threads and cores working on the same task. Now we've pulled back about as far as we can, and we're going to take a look at how to break up work in order to execute it on multiple computers. For large amounts of data, this can be especially useful.

In fact, big data has become more and more common. The definition of big data is a moving target as disk sizes grow, but you're working with big data if you have trouble processing it in memory or even storing it on disk. There's a lot of information locked in that much data, but getting it out can be a real problem. The recipes in this chapter will help address these issues.

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