# Great Books About Data Analysis

These are the textbooks that I love and 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.

## Python

- Think Python 2e Learn how to think as a computr scientist with python, by Allen B. Downey.
- 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.

## Javascript

- Javascript for Data Science an introduction to modern Javascript by Maya Gans, Toby Hodges, and Greg Wilson.
- D3 in Depth, visualize data on the web with D3, by Peter Cook.

## Git / Github

- 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.

## Project management

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

## Dataviz Design

- Data Visualization - A practical introduction Visualize data in R, by Kieran Healy.
- Scientific Color Palettes 🎨 Perceptually uniform colors, for scientific data visualization, by Fabio Crameri.
- Scico, Fabio Crameri’s color palettes ported to ggplot2, by Thomas Lin Pedersen.

## Typography

- Practical Typography by Matthew Butterick.

## Dashboards

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

## Computer Science

- Missing Semester A generic intro to basic CS productivity tips and tools, by Anish Athalye.

## Bayesian Statistics in R and Python

- A 10 minutes introduction to Bayesian statistics in R by Michael Clark.
- An introduction to Bayesian Thinking by Merlise Clyde et al.
- Think Bayes, an introduction to bayesian statistics in Python by Allen B. Downey.
- Bayesian Data Analysis by Andrew Gelman et al.

## Geocomputation

- Geocomputation with R; a book on geographic data analysis, visualization and modeling by Robin Lovelace, Jakub Nowosad and Jannes Muenchow.
- Spatial Data Science; concepts, packages and models for spatial data science in R, by Edzer Pebesma, Roger Bivand.

## More Books at Bookdown

- Check out the bookdown repository for many more.

# Source

Sourced from my github repo Great Books About Data Analysis, check it for the most updated version.