Great Books About Data Analysis

These are the books that I use as a daily reference. They are all openly accessible.

R

R for Data Science: An introduction to data analysis with R/Tidyverse by Hadley Wickham and Garret Grolemund.
- (2nd edition coming out soon).
Introduction to Data Science - A detailed introduction to Data science by the biostatistician Rafael A. Irizarry.
Advanced R - All you wish to know about programming in R by Hadley Wickham.
Introduction to Statistical Learning - A detailed introductio to modern statistical methods, implemented in R by Gareth James, Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft, Trevor Hastie and Rob Tibshirani.
Text Mining in R Analyzing natural language and written text in R, by Julia Silge and David Robinson.
Tidy Modeling with R An introduction to the tools that compose R’s machine learning framework, by Max Kuhn and Julia Silge.
Analising Data Using Linear Models, for students in social, behavioural and management science, by Stéphanie M. van den Berg.

The Python Data Science Handbook, foundation of python for data science, by Jake VanderPlas.
A Whirlwind Introduction to Python, a fast paced introduction to python, by Jake VanderPlas.
Python for Data Analysis, the basics of data analysis in Python, with numpy and pandas, by Wes McKinney.
Visualization Curriculum Data Visualization with Python, through Vega-Lite and Altair. Available also for javascript, by Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.

Happy Git and Github for useR by Jenny Brian and Jim Hester.
Pro Git Book, don’t worry, it starts from the basics; by Scott Chacon and Ben Straub.
Github Skills A set of practical exercise to learn Github.

Designing and Building Data Science Solutions how to set up a data science project, Jonathan Leslie and Neri Van Otten.

Dashboards with R + Docker + Github Actions by Rami Krispin, head of data science at Apple.