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

• 💡 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

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

• 📝 An Introduction to Docker for R Users by Colin Fay
• 🎓 R Docker tutorial by Jemma Stachelek
• ▶️ Docker and Python: making them play nicely and securely for Data Science and ML by Tania Allard at PyCon 2020

Markdown, LaTeX and publishing

• 📚 R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
• 📚 bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie
• 📚 The Not So Short Introduction to LaTeX 2ε by Tobias Oetiker
• 📚 LaTeX for Beginners by UoE IS Services

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

• 📝 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

Hiring and building teams

• 📚 The Care and Feeding of Data Scientists: How to Build, Manage and Retain a Data Science Team by Michelangelo D’Agostino and Katie Malone
• 🎧 The Care and Feeding of Data Scientists: Becoming a Data Science Manager on Linear Digressions podcast by Katie Malone and Ben Jaffe
• 📝 Models for integrating data science teams within companies by Pardis Noorzad
• 🎧 Building Effective Data Science Teams with Kobi Abayomi, Gregory Berg, Elaine McVey, Jacqueline Nolis, Nasir Uddin and Julia Silge
• 📝 Building a data team at a mid-stage startup: a short story by Erik Bernhardsson
• 📝 Hiring a data scientist by Mikhail Popov, Wikimedia

Agile data science

• 📚 Agile Data Science with R: A workflow by Edwin Thoen
• 📝 Data Science and Agile (What works, and what doesn’t) by Eugene Yan
• 📝 Data Science Best Practices: Run your data science team like an engineering team by Leonard Austin
• 📝 Organizing machine learning projects: project management guidelines by Jeremy Jordan

Ethics and fairness

• 📚 Ethics of Artificial Intelligence and Robotics by Stanford Encyclopedia of Philosophy
• 📝 The Responsible Machine Learning Principles: A practical framework to develop AI responsibly by The Institute for Ethical AI & Machine Learning
• 📝 A Code of Ethics for Data Science by DJ Patil
• 📝 The Ethical Data Scientist by Cathy O’ Neil
• 📝 An ethics checklist for data scientists by drivendata
• 📚 Fairness and machine learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan
• 📝 Practical Data Ethics by fast.ai

MLOps

• 📝 MLOps: Continuous delivery and automation pipelines in machine learning by Google Cloud
• 📝 Using GitHub Actions for MLOps & Data Science by Hamel Husain, The Github Blog
• 📝 Continuous Delivery for Machine Learning: Automating the end-to-end lifecycle of Machine Learning applications by Danilo Sato, Arif Wider and Christoph Windheuser
• 📝 Monitoring Machine Learning Models in Production: A Comprehensive Guide by Christopher Samiullah
• 📝 What are Azure Machine Learning pipelines? by Microsoft
• 📝 Getting started with Kubeflow Pipelines by Amy Unruh, Google Cloud
• 📝 Continuous Machine Learning (CML) is CI/CD for Machine Learning Projects by DVC.org
• 📝 Data Science Workflows by David Neuzerling
• 📝 Monitoring Machine Learning Models in Production A Comprehensive Guide by Christopher Samiullah

ML Platforms

• 📝 The problem with AI developer tools for enterprises (and what IKEA has to do with it) by Clemens Mewald
• 📝 5 Reasons Organizations Shouldn’t Build Their Own AI Platforms by dataiku

Style Guides

• 📝 Udacity Git Commit Message Style Guide by Udacity
• 📚 The tidyverse style guide by Hadley Wickham
• 📝The Google R Style Guide by Google
• 📝 The Google Python Style Guide by Google
• 📝 PEP 8 – Style Guide for Python Code by Guido van Rossum, Barry Warsaw, Nick Coghlan

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

• 📚 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

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

Spatial analysis

• 📚 Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
• 📚 Spatial Data Science by Edzer Pebesma and Roger Bivand
• 📚 Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny by Paula Moraga

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

Special Topics

• 🎮 Structural Equation Modelling by Erin M. Buchanan
• 📝 PyTorch Tutorials and Recipes by PyTorch