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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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 Duvvuri Duvvuri
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Duvvuri
 Singhal Singhal
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Singhal
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Table of Contents (18) Chapters Close

Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Chapter 7.  Extending Spark with SparkR

Statisticians and data scientists have been using R to solve challenging problems in almost every field, ranging from bioinformatics to election campaigns. They prefer R due to its powerful visualization capabilities, strong community, and rich package ecosystem for statistics and machine learning. Many academic institutions around the world teach data science and statistics using the R language.

R was originally created by and for statisticians in around the mid-1990s with a goal to deliver a better and more user-friendly way to perform data analysis. R was initially used in academics and research. As businesses became increasingly aware of the role of data science in their business growth, the number of data analysts using R in the corporate sector started growing as well. The R language user base is considered to be more than two million strong, after being in existence for two decades.

One of the driving factors behind all this success is the fact...

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