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Big Data Analysis with Python

You're reading from   Big Data Analysis with Python Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Length 276 pages
Edition 1st Edition
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Authors (3):
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Ivan Marin Ivan Marin
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Ivan Marin
Sarang VK Sarang VK
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Sarang VK
Ankit Shukla Ankit Shukla
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Ankit Shukla
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Table of Contents (11) Chapters Close

Big Data Analysis with Python
Preface
1. The Python Data Science Stack FREE CHAPTER 2. Statistical Visualizations 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

Correlation


Correlation is a statistical measure of the level of association between two numerical variables. It gives us an idea of how closely two variables are related with each other. For example, age and income are quite closely related variables. It has been observed that the average income grows with age within a threshold. Thus, we can assume that age and income are positively correlated with each other.

Note

However, correlation does not establish a cause-effect relationship. A cause-effect relationship means that one variable is causing a change in another variable.

The most common metric used to compute this association is the Pearson Product-Moment Correlation, commonly known as Pearson correlation coefficient or simply as the correlation coefficient. It is named after its inventor, Karl Pearson.

The Pearson correlation coefficient is computed by dividing the covariance of the two variables by the product of their standard deviations. The correlation value lies between -1 and +1...

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