Yet Another.. zi? I don't know what the title stands for but I do know it like a pretty fast and smooth TUI fm.

This book demonstrates how to use the Tidyverse collection of packages for doing data science.

A long and in-depth description of tidy data, how to arrive there, pitfalls to avoid. Also gives pointers on visualization and modeling.

Written for R, but a lot of the concepts can be applied universally.

Red is a next-generation programming language strongly inspired by Rebol, but with a broader field of usage thanks to its native-code compiler, from system programming to high-level scripting and cross-platform reactive GUI, while providing modern support for concurrency, all in a zero-install, zero-config, single ~1MB file!

Using tidy data principles is a powerful way to make handling data easier and more effective, and this is no less true when it comes to dealing with text.

An in-depth description of handling strings, textual data with the tidy principles. Really neat for applications such as NLP or sentiment analysis or text modeling.

Solutions to the exercises in “R for Data Science” by Garrett Grolemund and Hadley Wickham.

The book itself is also available online, here: https://r4ds.had.co.nz/

While written for R, the exercies could be easily adapted say for python pandas, and undertaken that way.