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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
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

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

References

  • The Spark Summit site has a wealth of information on Apache Spark and related projects from completed events
  • Interview with Matei Zaharia by KDnuggets
  • Why Spark Reached the Tipping Point in 2015 from KDnuggets by Matthew Mayo
  • Going Live: Preparing your first Spark production deployment is a very good starting point
  • What is Scala? from the Scala home page
  • Martin Odersky, creator of Scala, explains the reasons why Scala fuses together imperative and functional programming
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