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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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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

Descriptive statistics


In the previous section, we learnt how distributions are formed. In this section, we will learn how to describe them through descriptive statistics. There are two important components of a distribution that can help describe it, which are its location and its spread.

Measures of location

A measure of location is a single value that describes where the center of the data lies. The three most common measures of location are mean, median, and mode.

Mean

By far the most common and widely used measure of central tendency is the mean, which is otherwise known as the average. Whether it is a sample or a population, the mean or average is the summation of all the elements divided by the total number of elements.

Median

The median is the middle value of a series of data when sorted in any order so that half of the data is greater than the median and the other half smaller. When there are two middle values (with an even number of data items), the median is the average of those middle...

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