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Big Data Analysis with Python

You're reading from  Big Data Analysis with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Pages 276 pages
Edition 1st Edition
Languages
Authors (3):
Ivan Marin Ivan Marin
Profile icon Ivan Marin
Ankit Shukla Ankit Shukla
Profile icon Ankit Shukla
Sarang VK Sarang VK
Profile icon Sarang VK
View More author details

Table of Contents (11) Chapters

Big Data Analysis with Python
Preface
1. The Python Data Science Stack 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

Types of Graphs and When to Use Them


Every analysis, whether on small or large datasets, involves a descriptive statistics step, where the data is summarized and described by statistics such as mean, median, percentages, and correlation. This step is commonly the first step in the analysis workflow, allowing a preliminary understanding of the data and its general patterns and behaviors, providing grounds for the analyst to formulate hypotheses, and directing the next steps in the analysis. Graphs are powerful tools to aid in this step, enabling the analyst to visualize the data, create new views and concepts, and communicate them to a larger audience.

There is a vast amount of literature on statistics about visualizing information. The classic book, Envisioning Information, by Edward Tufte, demonstrates beautiful and useful examples of how to present information in graphical form. In another book, The Visual Display of Quantitative Information, Tufte enumerates a few qualities that a graph...

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