Abstract

If you are to perform any kind of data (statistical) analysis and use tools in the data science ecosystem, you may have seen a variety of tutorials, handbooks online (I’m not mentioning traditional bibles of statistics). Unfortunately most of them is written intentionally for specific purposes and/or target group of people and does not include many aspects, data types, difficult aspects, usually hiding them trying to solve theoretical problems of the matter. In this handbook I am not going to omit any of them. What is more, those, which are more problematic for data analyst will be underlined, explained in details, solved with the use of the newest libraries available at the time. Users who believe Python is the best tool for statistical analyses, probably might see some R advantages in the end of the study. We will start with preparing infrastructure for the analysis. Then, the quick tour through the R programming basics. Finally, two most important chapters of this book will walk you through the descriptive and inferential statistical data analysis. Online version is interactive: it means in many places reader will have the chance to go through tutorials, answer some questions, solve some tasks to go to the next page.

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