What this book covers
Chapter 1, Laying the Foundation for Reproducible Data Analysis, is a pretty important chapter, and I recommend that you do not skip it. It explains Anaconda, Docker, unit testing, logging, and other essential elements of reproducible data analysis.
Chapter 2, Creating Attractive Data Visualizations, demonstrates how to visualize data and mentions frequently encountered pitfalls.
Chapter 3, Statistical Data Analysis and Probability, discusses statistical probability distributions and correlation between two variables.
Chapter 4, Dealing with Data and Numerical Issues, is about outliers and other common data issues. Data is almost never perfect, so a large portion of the analysis effort goes into dealing with data imperfections. ...
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