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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details

Table of Contents (15) Chapters

Preface Installing Pyspark and Setting up Your Development Environment Getting Your Big Data into the Spark Environment Using RDDs Big Data Cleaning and Wrangling with Spark Notebooks Aggregating and Summarizing Data into Useful Reports Powerful Exploratory Data Analysis with MLlib Putting Structure on Your Big Data with SparkSQL Transformations and Actions Immutable Design Avoiding Shuffle and Reducing Operational Expenses Saving Data in the Correct Format Working with the Spark Key/Value API Testing Apache Spark Jobs Leveraging the Spark GraphX API Other Books You May Enjoy

Using Pearson and Spearman correlations to discover correlations

In this section, we will look at two different ways of computing correlations in your datasets, and these two methods are called Pearson and Spearman correlations.

The Pearson correlation

The Pearson correlation coefficient shows us how two different variables vary at the same time, and then adjusts it for how much they vary. This is probably one of the most popular ways to compute a correlation if you have a dataset.

The Spearman correlation

Spearman's rank correlation is not the default correlation...

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