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Python for Data Science For Dummies by Luca Massaron, John Paul Mueller

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Chapter 13

Exploring Data Analysis

In This Chapter

arrow Understanding the Exploratory Data Analysis (EDA) philosophy

arrow Describing numeric and categorical distributions

arrow Estimating correlation and association

arrow Testing mean differences in groups

arrow Visualizing distributions, relationships, and groups

Data science relies on complex algorithms for building predictions and spotting important signals in data, and each algorithm presents different strong and weak points. In short, you select a range of algorithms, you have them run on the data, you optimize their parameters as much as you can, and finally you decide which one will best help you build your data product or generate insight into your problem.

It sounds a little bit automatic and, partially, it is, thanks to powerful analytical software and scripting languages like Python. Learning algorithms are complex, and their sophisticated procedures naturally seem automatic and a bit opaque to you. However, even if some of these tools seem like ...

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