Free data science resources

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

🎓 = Course
📝 = Tutorial or blog post
📚 = Book or book chapter
▶️ = Video or webinar
🎧 = Podcast or audio recording
👥 = Community or user forum
📜 = Journal or technical article
💡 = Cheat sheet
✅ = List

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.
📝 RYouWithMe: Basic Basics by Lisa Williams, RLadies Sydney.
- Tour of RStudio, installing and using packages and getting data into RStudio.
🎓 Teacups, Statistics and Giraffes by Hasse Walum and Desirée de Leon.
- Accessible introduction to R and statistics with interactive coding exercises.
▶️ 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.
🎓 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.
- Learn R with interactive courses in the console.
🎓 Using R for Data Journalism by Andrew Ba Tran.
- Video supported intro course with emphasis on wrangling and visualisation.
📚 R for Data Science by Garrett Grolemund and Hadley Wickham.
- Comprehensive guide to using R programming for data science workflows.
📚 Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry.
- Introduction to data science focused topics in R: visualisation, wrangling, prediction and workflow.
💡 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.
- Get deeper into R programming fundamentals, object oriented and functional programming concepts and a lot more. A must-read for experience R users!
▶️ RStudio Webinars by RStudio.
- Recordings of past RStudio webinars covering a variety of R and data science content.
📚 An Introduction to R by W. N. Venables, D. M. Smith and the R Core Team.
- Introduction to R written by the R-Core team.
📚 / 🎓 Data science for economists by Grant McDermott.
- Slides and code examples covering wide ranging introduction to data science in R.
📚 / 🎓 Big Data in Economics by Grant McDermott.
- Notes cover the use of R with shell, GitHub, web scraping, docker and cloud compute.
📚 Handling Strings with R by Gaston Sanchez and Chitra Venkatesh.
- Detailed introduction to strings, manipulation, regex and text wrangling.
▶️ R Package Development by John Muschelli.
- 6-part video series on the basics of R package development, testing and building a pkgdown site.

Getting started with Python

📝 Install Python and Anaconda by Anaconda.
- The most commonly used package and environment manager for Python and how to install it.
🎓 Free interactive introduction to Python and pandas by ?.
- Beginners introduction to Python, pandas and data analysis via an interactive course.
📝 Quick reference to Python in a single script and notebook by Kevin Markham.
- Comprehensive reference guides for Python programming via notebooks and script examples.
🎓 / ▶️ An Introduction to Python and Programming by Alexander Hess.
- Python course for aspiring data scientists via notebooks, videos and exercises.
📚A Whirlwind Tour of Python by Jake VanderPlas.
- A fast-paced introduction to essential features of the Python language for those already familiar with another language.
🎓 Learn Python by Ron Reiter.
- Interactive online courses and tutorials for a wide range of Python topics.
💡 Pandas Cheat Sheet by the Pandas development team.
- 2-page quick reference to the most commonly used pandas functions.
📝 Getting Started in pandas by the Pandas development team.
Tutorials and quick start guides from the pandas development team.

Advancing with Python

📚 Python Data Science Handbook by Jake VanderPlas.
- Online book with comprehensive coverage of IPython, numpy, pandas, matplotlib and machine learning with scikit-learn.
📚 Python for Everybody: Exploring Data Using Python 3 by Charles R. Severance.
- Python ebook with a focus on programming fundamentals. Translations available in several languages.
📝 Python Packaging User Guide by the Python Packaging Authority (PyPA).
- A collection of tutorials and references to help you distribute and install Python packages with modern tools.

Shell

🎓 Learn Shell by Ron Reiter.
- A browser-based interactive Shell tutorial covering basics through to advanced topics.
🎓 The Unix Shell by Software Carpentry.
- Tutorials and examples of how to use the unix shell.
📝 Beginners/BashScripting by Ubuntu Documentation.
- Introduction to using the shell for OS navigation and scripting.
▶️ How to Write a Shell Script using Bash Shell in Ubuntu by FS Tutorial
- Short video showing how to write a first shell script using vim.
🎓 / ▶️ The Missing Semester of Your CS Education by Anish Athalye, Jon Gjengset and Jose Javier Gonzalez Ortiz
- Videos and notes on using shell and version control.
📝 The Art of the Command Line by Joshua Levy
- Useful list of bash commands and explanations, all laid out on a single page!
📝 ExplainShell.com by Idan Kamara
- Handy utility - type in a shell command and get an explanation of what it does.

Regular expressions

🎓 RegexOne: Learn Regular Expressions with simple, interactive exercises. by RegexOne
- Simple, browser based course with interactive exercises.
📝 Regular Expressions 101: Online Regular Expression Tester and Debugger by Firas Dib
- Very handy tool to test regular expressions against test strings.
💡 Data Science Cheat Sheet: Python Regular Expressions by Dataquest
- PDF cheat-sheet for standard regular expression syntax.
💡Regular Expressions Cheat Sheet by Dave Child
- PDF cheat-sheet for standard regular expression syntax.

Git

📚 Happy Git and GitHub for the useR by Jenny Bryan, the STAT 545 TAs and Jim Hester
- If you are an R user and new to git, this is currently the best place to start.
📝 An introduction to Git and how to use it with RStudio by François Michonneau
- Conceptual overview of what git is and how to use it, with particular emphasis on Github and its use with RStudio.
💡 Git Cheat Sheet by GitHub
- A list of the main git shell commands.
📚 Pro Git by Scott Chacon and Ben Straub
- Free ebook covering more advanced usage of git - good once you’re confident with the basics.
📝 Oh Shit Git! by Katie Sylor-Miller
- Light-hearted troubleshooting guide for when things inevitably go wrong!
📝 Step-by-step guide to contributing on GitHub by Kevin Markham
- Detailed guide on how to contribute to open source software projects using git and Github.

Spark

SQL

📚 / 🎓 The SQL Tutorial for Data Analysis by mode.com. Tutorials and interactive exercises 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

Docker

Markdown, LaTeX and publishing

Machine Learning

Theory

📚 The Elements of Statistical Learning: Data Mining, Inference, and Prediction 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).
- A statistical approach to data science and machine learning.
📚Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Covers the underpinning theory to many ML algorithms, a useful reference for practitioners.
📝 distill.pub by multiple contributors, edited by Shan Carter and Chris Olah
- Online scientific journal publishing very high-quality, interactive articles on ML. On hiatus as of 2021.
📚 Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
- Book based on Stanford Computer Science course CS246: Mining Massive Datasets.
📚 Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- ISLR is still one of the most important books for getting started in practical ML.

Interpretability

📚 Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar (2022)
- A highly practical introduction to IML, required reading if you are new to the topic.
✅ Awesome: Machine Learning Interpretability by Patrick Hall
- A big list of MLI resources with >2.5k github stars.

Guides, tutorials and courses

🎓 Machine Learning Crash Course with TensorFlow APIs by Google
- fast-paced, practical introduction to machine learning, with video lectures, real-world case studies, and hands-on practice exercises.
📝 Tidymodels Tutorials by RStudio
- Variety to beginners guides to solving common ML tasks with R’s tidymodels.
🎓 Supervised Machine Learning Case Studies in R by Julia Silge.
- Easy-to-follow in-browser beginner’s guide to using R’s tidymodels for practical ML.
📝 / 🎮 Introduction to machine learning with scikit-learn by Justin Markham
- Bite size study videos and python notebooks by Justin Markham’s Data School.
📝 scikit-learn User Guide by scikit-learn
- sci-kit learn’s documentation are very thorough and a great standalone learning resource!
🎓 Introduction to Machine Learning for Coders by Jeremy Howard.
- 24 hours of videos and supporting notes from a Kaggle superstar.

Data Science Practice

Software development

Hiring and building teams

Agile data science

Ethics and fairness

MLOps

ML Platforms

Style Guides

Developing interactive applications

🎮 / 🎓 Learn Shiny by RStudio
📚 A gRadual intRoduction to Shiny by Ted Laderas and Jessica Minnier
📚 Interactive web-based data visualization with R, plotly, and shiny by Carson Sievert
📚 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
📝 Getting Started with Streamlit by streamlit

Visualisation

Time series analysis

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

Spatial analysis

Data Science community groups

Python groups

👥 PyData Meetup Groups
👥 PyLadies by PyLadies

R groups

👥 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
- The R4DS Online Learning Community is a community of R learners at all skill levels working together to improve their skills.
👥 Tidy Tuesday
- A weekly podcast and community activity brought to you by the R4DS Online Learning Community.
👥SatRdays SatRdays +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

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