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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

Data visualization


Data visualization is something which is needed every now and then from the time you take on a data science assignment. Before building any model, preferably, you will have to visualize each variable to see their distributions to understand their characteristics and also find outliers so you can treat them. Simple tools such as scatterplot, box plot, bar chart, and so on are a few versatile, handy tools for such purposes. Also, you will have to use the visuals in most of the steps to ensure you are heading in the right direction.

Every time you want to collaborate with business users or stakeholders, it is always a good practice to convey your analysis through visuals. Visuals can accommodate more data in them in a more meaningful way and are inherently intuitive in nature.

Please note that most data science assignment outcomes are preferably represented through visuals and dashboards to business users. We already have a dedicated chapter on this topic, so we won't go deeper...

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