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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science FREE CHAPTER 2. Data Acquisition 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Using map-reduce

Map-reduce is a model for processing large sets of data in a parallel, distributed manner. This model consists of a map method for filtering and sorting data, and a reduce method for summarizing data. The map-reduce framework is effective because it distributes the processing of a dataset across multiple servers, performing mapping and reduction simultaneously on smaller pieces of the data. Map-reduce provides significant performance improvements when implemented in a multi-threaded manner. In this section, we will demonstrate a technique using Apache's Hadoop implementation. In the Using Java 8 to perform map-reduce section, we will discuss techniques for performing map-reduce using Java 8 streams.

Hadoop is a software ecosystem providing support for parallel computing. Map-reduce jobs can be run on Hadoop servers, generally set up as clusters, to significantly improve processing speeds. Hadoop has trackers that run map-reduce operations on nodes within a Hadoop...

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