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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

Summary


In this chapter, we have taken up a case study and completed the data analytics life cycle end to end. During the course of building a data product, we have applied the knowledge gained so far in the previous chapters. We have stated a business problem, formed an initial hypothesis, acquired data, and prepared it for model building. We have tried building multiple models and found a suitable model.

In the next chapter, which is also the final chapter, we will discuss building real-world applications using Spark.

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