- Overview
- Software & Programming
- Data Science Practice
- Developing interactive applications
- Visualisation
- Time series analysis
- Generalised Additive Modelling (GAMs)
- Statistics
- Data Science community groups
- Natural language processing
- Deep Learning
- Machine Learning
- Special Topics
π§ This page is a work in progress!
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
- Donβt require readers to sign up
Remember that material that is offered freely on the web is paid for by the authorβs 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
- π = Course
- π = Tutorial or blog post
- π = Book or book chapter
βΆοΈ = Video or webinar- π§ = Podcast or audio recording
- π₯ = Community or user forum
- π = Journal or technical article
- π‘ = Cheat sheet
- π 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.
- π RYouWithMe: Basic Basics by Lisa Williams, RLadies Sydney. Tour of RStudio, installing and using packages and getting data into RStudio.
- π Teacups, Statistics and Giraffes by Hasse Walum and DesirΓ©e de Leon
βΆοΈ A Gentle Introduction to Tidy Statistics in R by Thomas Mock, RStudio. Webinar covering exploratory data analysis, tidyverse, statistical testing and plotting.- π The R Bootcamp by Ted Laderas and Jessica Minnier. A tidyverse-centric interactive course for data manipulation, graphics, data reshaping, and statistical modelling.
- π Ready for R by Ted Laderas
- π RStudio Primers by RStudio. Interactive tutorials from RStudio covering data manipulation, visualisation and programming with R.
- π Swirl: Learn R, in R by Ismael FernΓ‘ndez, Nick Carchedi and Sean Kross
- π Using R for Data Journalism by Andrew Ba Tran
- π R for Data Science by Garrett Grolemund and Hadley Wickham
- π Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry
- π‘ Base R Cheat Sheet by Mhairi McNeill. Quick overview of basic R functionality.
- π Tidynomicon - A Brief Introduction to R for People Who Count From Zero by Greg Wilson. An introduction to R for Python users.
- π Hands-on Programming with R by Garrett Grolemund. A friendly introduction to the R language for non-programmers.
- π 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.
- π R package primer: a minimal tutorial by Karl Broman. Overview of R packages development.
- π R Packages by Hadley Wickham and Jennifer Bryan. Comprehensive guide to how R packages work and how to write your own.
- π Efficient R programming by Colin Gillespie and Robin Lovelace. Comprehensive introduction to writing faster and more efficient R code.
- π Advanced R by Hadley Wickham
βΆοΈ RStudio Webinars by RStudio- π An Introduction to R by W. N. Venables, D. M. Smith and the R Core Team
- π / π Data science for economists by Grant McDermott
- π / π Big Data in Economics by Grant McDermott
- π Handling Strings with R by Gaston Sanchez and Chitra Venkatesh
βΆοΈ R Package Development by John Muschelli
- π Install Python and Anaconda by Anaconda.
- π Free interactive introduction to Python and pandas.
- π Quick reference to Python in a single script and notebook by Kevin Markham.
- π An Introduction to Python and Programming by Alexander Hess.
- πA Whirlwind Tour of Python by Jake VanderPlas
- π Learn Python by Ron Reiter.
- π‘ Pandas Cheat Sheet by the Pandas development team.
- π 10 minutes to pandas by the Pandas development team
- π Python Data Science Handbook by Jake VanderPlas.
- π Python for Everybody: Exploring Data Using Python 3 by Charles R. Severance
- π Python Packaging User Guide by the Python Packaging Authority (PyPA)
- π Learn Shell by Ron Reiter.
- π The Unix Shell by Software Carpentry
- π The Beginnerβs Guide to Shell Scripting: The Basics by Yatri Trivedi
- π Beginners/BashScripting by Ubuntu Documentation
βΆοΈ How to Write a Shell Script using Bash Shell in Ubuntu by FS Tutorial- π /
βΆοΈ The Missing Semester of Your CS Education by Anish Athalye, Jon Gjengset and Jose Javier Gonzalez Ortiz - π The Art of the Command Line by Joshua Levy
- π ExplainShell.com by Idan Kamara
- π RegexOne: Learn Regular Expressions with simple, interactive exercises. by RegexOne
- π Regular Expressions 101: Online Regular Expression Tester and Debugger by Firas Dib
- π‘ Data Science Cheat Sheet: Python Regular Expressions by Dataquest
- π‘Regular Expressions Cheat Sheet by Dave Child
- π Getting started with Git and GitHub: the complete beginnerβs guide by Anne Bonner
- π An introduction to Git and how to use it with RStudio by FranΓ§ois Michonneau
- π‘ Git Cheat Sheet by GitHub
- π Pro Git by Scott Chacon and Ben Straub
- π Happy Git and GitHub for the useR by Jenny Bryan, the STAT 545 TAs, Jim Hester
- π‘ PySpark Cheat Sheet by Kevin Schaich
- π Mastering Spark with R by Javier Luraschi, Kevin Kuo and Edgar Ruiz
βΆοΈ R & Spark: How to Analyze Data Using RStudioβs Sparklyr by Nathan Stephens- πA Gentle Introduction to Spark by DataBricks
- π Learn JS by Ron Reiter
- π JavaScript for Data Science by Maya Gans, Toby Hodges, and Greg Wilson
- π / π The SQL Tutorial for Data Analysis by mode.com. Tutorials and interactive excercies teaching fundamentals of SQL.
- π SQLBolt: Learn SQL with simple, interactive exercises.
- π / π SQLZoo: SQL Tutorial. Wikibook with interactive exercises.
- π Intro to SQL: Querying and managing data by Khan Academy
- π LearnSQLOnline by Ron Reiter
- π An Introduction to Docker for R Users by Colin Fay
- π R Docker tutorial by ROpenSciLabs
βΆοΈ Docker and Python: making them play nicely and securely for Data Science and ML by Tania Allard at PyCon 2020
- π R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
- π bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie
- π The Not So Short Introduction to LaTeX 2Ξ΅ by Tobias Oetiker
- π LaTeX for Beginners by UoE IS Services
- π Software development skills for data scientists by Trey Causey
- π Hidden Technical Debt in Machine Learning Systems
- π How rOpenSci uses Code Review to Promote Reproducible Science by Noam Ross, Scott Chamberlain, Karthik Ram and MaΓ«lle Salmon
- π Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research by Victoria Stodden and Sheila Miguez
- π Journalism as a Professional Model for Data Science by Brian C. Keegan
- π Cookiecutter Data Science by drivendata
- π The Care and Feeding of Data Scientists: How to Build, Manage and Retain a Data Science Team by Michelangelo DβAgostino and Katie Malone
- π§ The Care and Feeding of Data Scientists: Becoming a Data Science Manager on Linear Digressions podcast by Katie Malone and Ben Jaffe
- π Agile Data Science with R: A workflow by Edwin Thoen
- π Data Science and Agile (What works, and what doesnβt) by Eugene Yan
- π Data Science Best Practices: Run your data science team like an engineering team by Leonard Austin
- π A Code of Ethics for Data Science by DJ Patil
- π The Ethical Data Scientist by Cathy Oβ Neil
- π An ethics checklist for data scientists by drivendata
- π Fairness and machine learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan
- π MLOps: Continuous delivery and automation pipelines in machine learning by Google Cloud
- π Using GitHub Actions for MLOps & Data Science by Hamel Husain, The Github Blog
- π Udacity Git Commit Message Style Guide by Udacity
- π The tidyverse style guide by Hadley Wickham
- πThe Google R Style Guide by Google
- π The Google Python Style Guide by Google
- π PEP 8 β Style Guide for Python Code by Guido van Rossum, Barry Warsaw, Nick Coghlan
βΆοΈ / π Learn Shiny by RStudio- π A gRadual intRoduction to Shiny by Ted Laderas and Jessica Minnier
- π Interactive web-based data visualization with R, plotly, and shiny by Carson Sievert
- π Dashboards by Yihui Xie, J. J. Allaire, Garrett Grolemund. Chapter 5 from βR Markdown: The Definitive Guideβ.
- π Leaflet for R by RStudio
- π Dash User Guide by Plotly
- π Fundamentals of Data Visualization by Claus O. Wilke
- π ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
- π 3D Mapping and Visualization with R and Rayshader by Tyler Morgan-Wall
- π Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
- π 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet) by Jason Brownlee
- π GAMs in R by Noam Ross Interactive course introducing Generalised Additive Models (GAMs).
- π Resources for Learning About and Using GAMs in R by Noam Ross
- π Statistical Inference via Data Science: A Modern Dive into R and the tidyverse by Chester Ismay and Albert Y. Kim
- π Think Stats Exploratory Data Analysis in Python by Allen B. Downey
- π Learning statistics with R: A tutorial for psychology students and other beginners Danielle Navarro
- π Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon
- π From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science by Norm Matloff
- π Theory of Statistics by James E. Gentle
- π Core Statistics by Simon Wood
- π₯ PyData Meetup Groups
- π₯ PyLadies by PyLadies
- π₯ Directory of R User Groups by Jumping Rivers
- π₯ Complete list of R-Ladies groups by R-Ladies Global
- π₯ R for Data Science Online Learning Community
- π₯ Tidy Tuesday A weekly podcast and community activity brought to you by the R4DS Online Learning Community
- π₯SatRdays SatRdays are R-focused conferences that are held on Saturdays
- π Text Mining with R: A Tidy Approach by Julia Silge and David Robinson
- π Advanced NLP with SpaCy by Ines Montani
- π 100 Must read papers in NLP by Masato Hagiwara
- π Stanford CS 124: From Languages to Information by Dan Jurafsky
- π Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper.
- π 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.
- π Speech and Language Processing by Dan Jurafsky and James H. Martin
βΆοΈ BERT Research Series by Chris McCormick
- π Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2017)
- π Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie (2017).
- πMathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- π Distill
- π Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
- π Machine Learning Crash Course with TensorFlow APIs by Google
- π Supervised Machine Learning Case Studies in R by Julia Silge.
- π /
βΆοΈ Introduction to machine learning with scikit-learn by Justin Markham - π scikit-learn User Guide by scikit-learn
- π Introduction to Machine Learning for Coders by Jeremy Howard.
- π Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar (2020)
- π Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- π Machine Learning Interpretability (MLI) by h2o.ai