Free data science resources
- Overview
- Software & Programming
- Data Science Software Development
- Data Science Ethics
- Developing interactive applications
- Visualisation
- GIS and Spatial Data Science
- Time series analysis
- Generalised Additive Modelling (GAMs)
- Statistics
- Data Science community groups
- Natural language processing
- Machine Learning
- Special Topics
🚧 This page is a work in progress!
Overview
The goal of this page is to gather resources and learning materials across a broad range of popular data science topics and arrange them thematically. Resources have been selected because they are
- High quality
- Free of charge
- Don’t require readers to sign up
Remember that material that is offered freely on the web is paid for by the author’s time - if you find a resource particularly useful, consider supporting them in whatever way they prefer. If you find this page useful please share it and spread the word! If you find a mistake or broken link, please file an issue or submit a pull request.
Key to resource types
▶️ = Video or webinar- 🎓 = Course
- 📝 = Tutorial or blog post
- 📚 = Book or book chapter
- 👥 = Community or user forum
- 📜 = Journal or technical article
- 💡 = Cheat sheet
Software & Programming
Getting started with R
- 📚 Modern Dive: Getting Started by Chester Ismay and Albert Y. Kim. The very first of first steps. Install R & RStudio and what to do after that.
- 📝 Basic Basics by R Ladies Sydney. Tour of RStudio, installing and using packages and getting data into RStudio.
- 🎓 Teacups, Statistics and Giraffes by Hasse Walum and Desirée de Leon
▶️ A Gentle Introduction to Tidy Statistics in R by Thomas Mock, RStudio. Webinar covering exploratory data analysis, tidyverse, statistical testing and plotting.- 🎓 The R Bootcamp by Ted Laderas and Jessica Minnier. A tidyverse-centric interactive course for data manipulation, graphics, data reshaping, and statistical modelling.
- 🎓 Ready for R by Ted Laderas
- 🎓 RStudio Primers by RStudio. Interactive tutorials from RStudio covering data manipulation, visualisation and programming with R.
- 🎓 Swirl: Learn R, in R by Ismael Fernández, Nick Carchedi and Sean Kross
- 🎓 Using R for Data Journalism by Andrew Ba Tran
- 📚 R for Data Science by Garrett Grolemund and Hadley Wickham
- 💡 Base R Cheat Sheet by Mhairi McNeill. Quick overview of basic R functionality.
Advancing with R
- 📚 Tidynomicon - A Brief Introduction to R for People Who Count From Zero by Greg Wilson. An introduction to R for Python users.
- 📚 Hands-on Programming with R by Garrett Grolemund. A friendly introduction to the R language for non-programmers.
- 📚 R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics by James (JD) Long, Paul Teetor. Recipes and worked examples for performing core tasks in R.
- 📝 R package primer: a minimal tutorial by Karl Broman. Overview of R packages development.
- 📚 R Packages by Hadley Wickham and Jennifer Bryan. Comprehensive guide to how R packages work and how to write your own.
- 📚 Efficient R programming by Colin Gillespie and Robin Lovelace. Comprehensive introduction to writing faster and more efficient R code.
- 📚 Advanced R by Hadley Wickham
▶️ RStudio Webinars by RStudio- 📚 An Introduction to R by W. N. Venables, D. M. Smith and the R Core Team
- 📚 / 🎓 Data science for economists by Grant McDermott
- 📚 / 🎓 Big Data in Economics by Grant McDermott
Getting started with Python
- 📝 Install Python and Anaconda by Anaconda.
- 🎓 Free interactive introduction to Python and pandas.
- 📝 Quick reference to Python in a single script and notebook by Kevin Markham.
- 📝 An Introduction to Python and Programming by Alexander Hess.
- 🎓 Learn Python by Ron Reiter.
- 💡 Pandas Cheat Sheet by the Pandas development team.
- 📝 10 minutes to pandas by the Pandas development team
Advancing with Python
- 📚 Python Data Science Handbook by Jake VanderPlas.
- 📚 Python for Everybody: Exploring Data Using Python 3 by Charles R. Severance
Shell
- 🎓 Learn Shell by Ron Reiter.
- 🎓 The Unix Shell by Software Carpentry
- 📝 The Beginner’s Guide to Shell Scripting: The Basics by Yatri Trivedi
- 📝 Beginners/BashScripting by Ubuntu Documentation
▶️ How to Write a Shell Script using Bash Shell in Ubuntu by FS Tutorial
Regular expressions
- 🎓 RegexOne: Learn Regular Expressions with simple, interactive exercises. by RegexOne
- 📝 Regular Expressions 101: Online Regular Expression Tester and Debugger by Firas Dib
- 💡 Data Science Cheat Sheet: Python Regular Expressions by Dataquest
- 💡Regular Expressions Cheat Sheet by Dave Child
Git
- 📝 Getting started with Git and GitHub: the complete beginner’s guide by Anne Bonner
- 📝 An introduction to Git and how to use it with RStudio by François Michonneau
- 💡 Git Cheat Sheet by GitHub
- 📚 Pro Git by Scott Chacon and Ben Straub
- 📚 Happy Git and GitHub for the useR by Jenny Bryan, the STAT 545 TAs, Jim Hester
Spark
- 💡 PySpark Cheat Sheet by Kevin Schaich
- 🎓 Mastering Spark with R by Javier Luraschi, Kevin Kuo and Edgar Ruiz
▶️ R & Spark: How to Analyze Data Using RStudio’s Sparklyr by Nathan Stephens- 📚A Gentle Introduction to Spark by DataBricks
Javascript
SQL
- 📚 / 🎓 The SQL Tutorial for Data Analysis by mode.com. Tutorials and interactive excercies teaching fundamentals of SQL.
- 🎓 SQLBolt: Learn SQL with simple, interactive exercises.
- 📚 / 🎓 SQLZoo: SQL Tutorial. Wikibook with interactive exercises.
- 🎓 Intro to SQL: Querying and managing data by Khan Academy
- 🎓 LearnSQLOnline by Ron Reiter
Data Science Software Development
- 📝 Software development skills for data scientists by Trey Causey
- 📜 Hidden Technical Debt in Machine Learning Systems
- 📝 How rOpenSci uses Code Review to Promote Reproducible Science by Noam Ross, Scott Chamberlain, Karthik Ram and Maëlle Salmon
- 📜 Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research by Victoria Stodden and Sheila Miguez
- 📝 Journalism as a Professional Model for Data Science by Brian C. Keegan
- 📝 Cookiecutter Data Science by drivendata
Data Science Ethics
- 📝 A Code of Ethics for Data Science by DJ Patil
- 📝 The Ethical Data Scientist Cathy O’ Neil
- 📝 An ethics checklist for data scientists by drivendata
Developing interactive applications
- ⏯ / 🎓 Learn Shiny by RStudio
- 📚 A gRadual intRoduction to Shiny by Ted Laderas and Jessica Minnier
- 📚 Dashboards by Yihui Xie, J. J. Allaire, Garrett Grolemund. Chapter 5 from ‘R Markdown: The Definitive Guide’.
- 📝 Leaflet for R by RStudio
- 📝 Dash User Guide by Plotly
Visualisation
- 📚 Fundamentals of Data Visualization by Claus O. Wilke
- 📚 ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
- 📝 3D Mapping and Visualization with R and Rayshader by Tyler Morgan-Wall
GIS and Spatial Data Science
Time series analysis
- 📚 Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
- 📝 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet) by Jason Brownlee
Generalised Additive Modelling (GAMs)
- 🎓 GAMs in R by Noam Ross Interactive course introducing Generalised Additive Models (GAMs).
- 📝 Resources for Learning About and Using GAMs in R by Noam Ross
Statistics
- 📚 Statistical Inference via Data Science: A Modern Dive into R and the tidyverse by Chester Ismay and Albert Y. Kim
- 📚 Think Stats Exploratory Data Analysis in Python by Allen B. Downey
- 📚 Learning statistics with R: A tutorial for psychology students and other beginners Danielle Navarro
- 📚 Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon
- 📚 From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science by Norm Matloff
- 📚 Theory of Statistics by James E. Gentle
- 📚 Core Statistics by Simon Wood
Data Science community groups
- 👥 PyData Meetup Groups
- 👥 PyLadies by PyLadies
- 👥 Directory of R User Groups by Jumping Rivers
- 👥 Complete list of R-Ladies groups by R-Ladies Global
- 👥 R for Data Science Online Learning Community
- 👥 Tidy Tuesday A weekly podcast and community activity brought to you by the R4DS Online Learning Community
- 👥SatRdays SatRdays are R-focused conferences that are held on Saturdays
Natural language processing
- 📚 Text Mining with R: A Tidy Approach by Julia Silge and David Robinson
- 🎓 Advanced NLP with SpaCy by Ines Montani
- 📜 100 Must read papers in NLP by Masato Hagiwara
- 🎓 Stanford CS 124: From Languages to Information by Dan Jurafsky
- 📚 Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper.
- 🎓 A Code-First Intro to Natural Language Processing by fast.ai. The course is taught in Python with Jupyter Notebooks, using libraries such as sklearn, nltk, pytorch, and fastai.
- 📚 Speech and Language Processing by Dan Jurafsky and James H. Martin
▶️ BERT Research Series by Chris McCormick
Machine Learning
Theory
- 📚 Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2017)
- 📚 Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie (2017).
- 📚Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- 📝 Distill
- 📚 Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
Practice
- 🎓 Supervised Machine Learning Case Studies in R by Julia Silge.
- 📝 / 🎮 Introduction to machine learning with scikit-learn by Justin Markham
- 📝 scikit-learn User Guide by scikit-learn
- 🎓 Introduction to Machine Learning for Coders by Jeremy Howard.
- 📚 Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar (2020)
- 📚 Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani