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.

Authors

Support the authors of these textbooks with the means that are available to you, they are heroes.