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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

In general, designing and creating a computer system is a balancing act. We're constantly trying to add features and capabilities while keeping the code simple and the system's performance reasonable. In this respect, data analysis systems are no different. In fact, they may be worse. Often data is only partially consistent, and we need to employ a variety of strategies to extract usable data before we can even begin its analysis. Each added strategy adds a little more complexity, weight, and bloat to the code, each of which makes it a little harder to maintain. This can get out of hand.

Clojure has a number of libraries to help us manage our systems' complexity. One of the most powerful of these is concurrent programming. This allows us to conceptualize our programs differently and in ways that can help manage the complexity. Instead of having monolithic blocks of code that do many things and have direct, tight dependencies, we can structure our program more...

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