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.
• 🎓 pandas Tutorial by W3Schools.
  • Beginner-friendly, in-browser introduction to pandas with interactive “try it yourself” examples.
• 📝 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.

Polars

• 📝 Polars User Guide by the Polars team
  • The official, example-driven guide to Polars’ expressions, contexts and lazy execution.
• 📚 Modern Polars by Kevin Heavey
  • A side-by-side comparison of idiomatic Polars and pandas, with commentary on API design and performance.

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.

Web scraping

• 📚 Automate the Boring Stuff with Python (3e): Web Scraping by Al Sweigart
  • A beginner-friendly, project-driven walkthrough of downloading and parsing web pages with requests, Beautiful Soup, Selenium and Playwright.
• 📚 R for Data Science (2e): Web scraping by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund
  • A clear introduction to extracting data from HTML pages in R with the rvest package, including selectors and scraping ethics.

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
  • A handy single-page reference of the most commonly used PySpark DataFrame operations.
• 🎓 Mastering Spark with R by Javier Luraschi, Kevin Kuo and Edgar Ruiz
  • Comprehensive guide to analysing large-scale data from R using the sparklyr package.
• ▶️ R & Spark: How to Analyze Data Using RStudio’s Sparklyr by Nathan Stephens
  • Webinar demonstrating how to connect to Spark and analyse data from R with sparklyr.
• 📚A Gentle Introduction to Apache Spark by Databricks
  • Short ebook introducing Apache Spark’s core concepts, architecture and APIs.

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.
  • Learn SQL from the ground up through a series of short, interactive browser-based lessons.
• 📚 / 🎓 SQLZoo: SQL Tutorial. Wikibook with interactive exercises.
• 📚 / 🎓 Select Star SQL by Zi Chong Kao.
  • Free, ad-free interactive book that teaches SQL through real-world datasets in the browser.
• 🎓 Intro to SQL: Querying and managing data by Khan Academy
  • Video-based course on querying and managing data with SQL.
• 🎓 LearnSQLOnline by Ron Reiter
  • Browser-based interactive tutorial covering SQL fundamentals.

DuckDB

• 📝 DuckDB Documentation by the DuckDB team
  • The official documentation hub covering installation, SQL and client APIs across many languages.
• 📝 SQL Introduction by the DuckDB team
  • A hands-on tour of core SQL operations – tables, queries, joins and aggregates – using DuckDB.

Docker

• 📝 An Introduction to Docker for R Users by Colin Fay
  • A short guide to using Docker to make R analyses portable and reproducible.
• 🎓 R Docker tutorial by Jemma Stachelek
  • Hands-on tutorial on running R and RStudio inside Docker containers.
• ▶️ Docker and Python: making them play nicely and securely for Data Science and ML by Tania Allard at PyCon 2020
  • Talk on building Docker images for Python data science work safely and efficiently.

Markdown, LaTeX and publishing

• 📚 R Markdown: The Definitive Guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
  • The comprehensive reference for authoring reproducible documents and reports with R Markdown.
• 📚 bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie
  • Guide to writing books and long-form technical documents with R Markdown.
• 📝 Get Started with Quarto by Posit
  • The official starting point for authoring and publishing reproducible documents, reports and slides with Quarto.
• 📚 R for Data Science (2e): Quarto by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund
  • A concise, practical chapter on weaving code, results and prose into a single reproducible document.
• 📚 Quarto for Scientists by Nicholas Tierney
  • A gentle, scientist-focused guide to producing reproducible reports, papers and slides with Quarto.
• 📚 The Not So Short Introduction to LaTeX 2ε by Tobias Oetiker
  • A concise and practical introduction to typesetting documents with LaTeX.
• 📚 LaTeX for Beginners by UoE IS Services
  • Step-by-step primer for getting started with LaTeX.

Data engineering and pipelines

• 🎓 Data Engineering Zoomcamp by DataTalksClub
  • A free, end-to-end course building production-ready pipelines with dbt, orchestration, BigQuery, Spark and Kafka.
• 📚 dbt Documentation by dbt Labs
  • The official documentation for building, testing and deploying analytics transformations with dbt.
• 📝 Apache Airflow Tutorials by the Apache Software Foundation
  • The official hands-on guides for authoring your first workflows and DAGs in Apache Airflow.
• 📚 The {targets} R Package User Manual by Will Landau
  • A thorough guide to building reproducible, dependency-aware analysis pipelines in R.
• 📝 minimal make: A minimal tutorial on make by Karl Broman
  • A short, approachable tutorial on using GNU Make to automate reproducible research workflows.

Machine Learning

Theory

• 📚 The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2017)
  • The classic, mathematically rigorous reference on statistical and machine learning.
• 📚 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.

Deep learning

• 📚 Dive into Deep Learning by Aston Zhang, Zachary Lipton, Mu Li and Alexander Smola
  • Interactive deep learning book with runnable code across PyTorch, TensorFlow and JAX.
• 📚 Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
  • The foundational graduate-level textbook on the theory behind modern deep learning.
• 🎓 Practical Deep Learning for Coders by Jeremy Howard, fast.ai
  • Top-down course teaching state-of-the-art deep learning for vision, text and tabular data with PyTorch.
• ▶️ Neural Networks: Zero to Hero by Andrej Karpathy
  • Video series building neural networks, and eventually a GPT, from scratch starting at backpropagation.
• 🎓 MIT 6.S191: Introduction to Deep Learning by Alexander Amini and Ava Soleimany
  • MIT’s introductory deep learning course with open lectures, slides and software labs.

Computer vision

• 🎓 CS231n: Deep Learning for Computer Vision by Stanford University
  • Stanford’s computer vision course, with freely available lecture notes, Python primers and assignments.
• 📝 OpenCV Tutorials by the OpenCV team
  • Official tutorials spanning image processing, object detection, feature matching and the deep neural network module.

Reinforcement learning

• 📚 Reinforcement Learning: An Introduction (2nd ed.) by Richard S. Sutton and Andrew G. Barto
  • The standard introductory textbook on reinforcement learning, offered as a complete free PDF by its authors.
• ▶️ Introduction to Reinforcement Learning by David Silver, DeepMind
  • A ten-lecture course covering reinforcement learning fundamentals through to function approximation and policy gradients.
• 📝 Spinning Up in Deep RL by OpenAI
  • An educational resource on deep RL with explanatory theory, a curated paper list and documented reference implementations.

Large language models and generative AI

• 🎓 Hugging Face LLM Course by Hugging Face
  • Hands-on course on large language models and NLP using the Hugging Face libraries.
• 📝 The Illustrated Transformer by Jay Alammar
  • A visual, intuitive walkthrough of the Transformer architecture and self-attention.
• 📚 Understanding Deep Learning by Simon J. D. Prince
  • Modern textbook covering transformers, diffusion and generative models, with a free PDF and notebooks.
• 🎓 Stanford CS224N: NLP with Deep Learning by Christopher Manning
  • Stanford’s NLP with deep learning course, with freely available slides, assignments and lecture videos.
• 📝 Prompt Engineering Guide by DAIR.AI
  • Reference guide to prompting techniques, applications and risks for large language models.
• 📝 Building Effective Agents by Anthropic
  • A practical breakdown of agent and workflow patterns, with guidance on when added complexity is worth it.
• 📝 Claude Cookbooks by Anthropic
  • Runnable notebooks covering retrieval, tool use, agents, evaluations and multimodal work with Claude.
• 🎓 Anthropic Courses by Anthropic
  • Sequential courses on API fundamentals, prompt engineering, prompt evaluations and tool use.
• 📝 Patterns for Building LLM-based Systems & Products by Eugene Yan
  • A survey of practical patterns for production LLM systems, from evaluation and retrieval to guardrails and feedback.
• 📝 Your AI Product Needs Evals by Hamel Husain
  • A grounded framework for evaluating LLM products, built around unit tests, human and model review, and A/B testing.

Data Science Practice

Software development

• 📝 Software development skills for data scientists by Trey Causey
  • Practical overview of the software engineering skills data scientists should develop.
• 📜 Hidden Technical Debt in Machine Learning Systems
  • Influential paper on the hidden, long-term maintenance costs of machine learning systems.
• 📝 How rOpenSci uses Code Review to Promote Reproducible Science by Noam Ross, Scott Chamberlain, Karthik Ram and Maëlle Salmon
  • How rOpenSci runs open peer review of scientific R packages.
• 📜 Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research by Victoria Stodden and Sheila Miguez
  • Recommendations for building reproducible and extensible computational research software.
• 📝 Journalism as a Professional Model for Data Science by Brian C. Keegan
  • Argues data science teams can learn from how newsrooms are organised and managed.
• 📝 Cookiecutter Data Science by drivendata
  • A standardised, logical project structure for doing and sharing data science work.

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
  • Practical report on building, managing and retaining a data science team.
• 🎧 The Care and Feeding of Data Scientists: Becoming a Data Science Manager on Linear Digressions podcast by Katie Malone and Ben Jaffe
  • Podcast episode on the transition from data scientist to data science manager.
• 📝 Models for integrating data science teams within companies by Pardis Noorzad
  • Compares centralised, embedded and hybrid models for placing data science teams in an organisation.
• 🎧 Building Effective Data Science Teams with Kobi Abayomi, Gregory Berg, Elaine McVey, Jacqueline Nolis, Nasir Uddin and Julia Silge
  • Panel webinar on what makes data science teams effective.
• 📝 Building a data team at a mid-stage startup: a short story by Erik Bernhardsson
  • A short story illustrating how to grow a data team at a startup.
• 📝 Hiring a data scientist by Mikhail Popov, Wikimedia
• Wikimedia’s practical account of how they hire data scientists.

Agile data science

• 📚 Agile Data Science with R: A workflow by Edwin Thoen
  • Proposes a lightweight, agile workflow for data science projects in R.
• 📝 Data Science and Agile (What works, and what doesn’t) by Eugene Yan
  • Honest reflection on which agile practices help, and which hinder, data science work.
• 📝 Data Science Best Practices: Run your data science team like an engineering team by Leonard Austin
  • Argues for running a data science team with the discipline of an engineering team.
• 📝 Organizing machine learning projects: project management guidelines by Jeremy Jordan
  • Project-management guidelines for structuring machine learning projects.

Ethics and fairness

• 📚 Ethics of Artificial Intelligence and Robotics by Stanford Encyclopedia of Philosophy
  • A thorough philosophical survey of the ethical issues raised by AI and robotics.
• 📝 The Responsible Machine Learning Principles: A practical framework to develop AI responsibly by The Institute for Ethical AI & Machine Learning
  • A practical framework of principles for developing AI responsibly.
• 📝 A Code of Ethics for Data Science by DJ Patil
  • A proposed code of ethics for practising data scientists.
• 📝 The Ethical Data Scientist by Cathy O’ Neil
  • Essay arguing data scientists should take responsibility for the impact of their models.
• 📝 An ethics checklist for data scientists by drivendata
  • A command-line tool that adds an ethics checklist to your data science projects.
• 📚 Fairness and machine learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan
  • A textbook on the limitations and opportunities of fairness in machine learning.
• 📝 Practical Data Ethics by fast.ai
  • A practical course on data ethics covering bias, disinformation and accountability.

MLOps

• 📝 MLOps: Continuous delivery and automation pipelines in machine learning by Google Cloud
  • Google Cloud’s reference on continuous delivery and automation pipelines for machine learning.
• 📝 Using GitHub Actions for MLOps & Data Science by Hamel Husain, The Github Blog
  • How to use GitHub Actions to automate data science and machine learning workflows.
• 📝 Continuous Delivery for Machine Learning: Automating the end-to-end lifecycle of Machine Learning applications by Danilo Sato, Arif Wider and Christoph Windheuser
  • A detailed pattern for automating the end-to-end machine learning lifecycle.
• 📝 Monitoring Machine Learning Models in Production: A Comprehensive Guide by Christopher Samiullah
  • Comprehensive guide to monitoring machine learning models once they are deployed.
• 📝 What are Azure Machine Learning pipelines? by Microsoft
  • Microsoft’s overview of building machine learning pipelines in Azure.
• 📝 Getting started with Kubeflow Pipelines by Amy Unruh, Google Cloud
  • Getting started with building and running machine learning pipelines on Kubeflow.
• 📝 Continuous Machine Learning (CML) is CI/CD for Machine Learning Projects by DVC.org
  • Open-source tool bringing CI/CD practices to machine learning projects.
• 📝 Data Science Workflows by David Neuzerling
  • A practical look at structuring reproducible data science workflows.
• 🎓 Made With ML by Goku Mohandas
  • Course combining machine learning and software engineering to design, deploy and iterate on production ML.

ML Platforms

• 📝 The problem with AI developer tools for enterprises (and what IKEA has to do with it) by Clemens Mewald
  • Argues enterprise AI tooling should be modular and composable rather than monolithic.

Style Guides

• 📝 Udacity Git Commit Message Style Guide by Udacity
  • A concise convention for writing clear, consistent git commit messages.
• 📚 The tidyverse style guide by Hadley Wickham
  • The style guide for writing readable, consistent tidyverse-flavoured R code.
• 📝The Google R Style Guide by Google
  • Google’s conventions for formatting and structuring R code.
• 📝 The Google Python Style Guide by Google
  • Google’s conventions for writing clean, consistent Python code.
• 📝 PEP 8 – Style Guide for Python Code by Guido van Rossum, Barry Warsaw, Nick Coghlan
  • The official style guide for Python code.

Developing interactive applications

• ▶️ / 🎓 Learn Shiny by RStudio
  • Official tutorial for building interactive web apps in R with Shiny.
• 📚 A gRadual intRoduction to Shiny by Ted Laderas and Jessica Minnier
  • A gentle, step-by-step introduction to building Shiny apps.
• 📚 Interactive web-based data visualization with R, plotly, and shiny by Carson Sievert
  • Book on building interactive, web-based data visualisations with R, plotly and Shiny.
• 📚 Dashboards by Yihui Xie, J. J. Allaire, Garrett Grolemund. Chapter 5 from ‘R Markdown: The Definitive Guide’.
• 📝 Leaflet for R by RStudio
  • Guide to making interactive maps in R with the Leaflet library.
• 📝 Dash User Guide by Plotly
  • Documentation for building analytical web applications in Python with Dash.
• 📝 Getting Started with Streamlit by streamlit
  • Quick start guide for turning Python scripts into shareable data apps with Streamlit.

Visualisation

• 📚 Fundamentals of Data Visualization by Claus O. Wilke
  • A guide to making clear, compelling and truthful figures, with examples in ggplot2.
• 📚 ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
  • The definitive guide to producing graphics in R with ggplot2.
• 📚 Data Visualization: A Practical Introduction by Kieran Healy
  • A practical, hands-on introduction to data visualisation principles using R and ggplot2.
• 📝 3D Mapping and Visualization with R and Rayshader by Tyler Morgan-Wall
  • Masterclass materials on 3D mapping and visualisation in R with rayshader.
• 📚 Scientific Visualization: Python + Matplotlib by Nicolas P. Rougier
  • An open-access book on producing high-quality scientific figures with matplotlib.
• 📝 seaborn: statistical data visualization by Michael Waskom
  • The official tutorial for seaborn, covering statistical plotting built on matplotlib.
• 📝 The Python Graph Gallery by Yan Holtz
  • Hundreds of Python chart examples with reproducible code across matplotlib, seaborn, pandas and plotly.
• 📝 Plotly Open Source Graphing Library for Python by Plotly
  • Official documentation for Plotly’s free, open-source library for interactive charts.

Time series analysis

• 📚 Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
  • The standard free textbook on time series forecasting, with worked examples in R.
• 📝 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet) by Jason Brownlee
  • A cheat-sheet of classical time series forecasting methods with Python code.

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
  • A curated list of resources for learning about and using GAMs in R.

Mathematics

• ▶️ Essence of Linear Algebra by Grant Sanderson (3Blue1Brown)
  • A visual, intuition-first video series on vectors, transformations, determinants and eigenvectors.
• ▶️ Essence of Calculus by Grant Sanderson (3Blue1Brown)
  • A visual series building the central ideas of calculus from derivatives to integrals.
• 🎓 Linear Algebra (18.06) by Gilbert Strang, MIT OpenCourseWare
  • Strang’s classic full-semester course with complete video lectures, problem sets and exams.
• 📚 Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe
  • A free textbook on the linear algebra most relevant to data science, with an emphasis on least squares.
• ▶️ Statistics 110: Probability by Joe Blitzstein, Harvard
  • A full set of Harvard lectures teaching probability from first principles.

Statistics

• 📚 Statistical Inference via Data Science: A Modern Dive into R and the tidyverse by Chester Ismay and Albert Y. Kim
  • A modern, tidyverse-based introduction to statistical inference in R.
• 📚 Think Stats Exploratory Data Analysis in Python by Allen B. Downey
  • An introduction to exploratory data analysis and statistics using Python.
• 📚 Learning statistics with R: A tutorial for psychology students and other beginners Danielle Navarro
  • A friendly, thorough introduction to statistics using R, aimed at beginners.
• 📚 Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon
• A computation-first introduction to Bayesian methods using Python and PyMC.
• 📚 From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science by Norm Matloff
  • A probability and statistics text that builds concepts through computer science examples.
• 📚 Theory of Statistics by James E. Gentle
  • A rigorous graduate-level reference on the theory of mathematical statistics.
• 📚 Core Statistics by Simon Wood
  • A compact introduction to the core theory and practice of statistical inference.
• ▶️ / 🎓 Statistical Rethinking by Richard McElreath
  • Freely available video lectures, slides and homework teaching Bayesian modelling from first principles.
• 📚 Think Bayes 2 by Allen B. Downey
  • An introduction to Bayesian statistics through computation in Python.
• 📚 Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia Johnson, Miles Ott and Mine Dogucu
  • An applied introduction to Bayesian modelling in R, covering MCMC and hierarchical models.

Causal inference

• 📚 Causal Inference: The Mixtape by Scott Cunningham
  • An applied introduction to causal inference and research designs, with code in R, Stata and Python.
• 📚 Causal Inference for The Brave and True by Matheus Facure
  • An open-source, Python-based introduction to causal inference, from fundamentals to ML methods.
• 📚 The Effect: An Introduction to Research Design and Causality by Nick Huntington-Klein
  • An accessible introduction to research design and causality, with code in R, Stata and Python.

Experimentation and A/B testing

• 📝 How Not To Run an A/B Test by Evan Miller
  • A concise, widely-cited explanation of why peeking at running experiments inflates false positives, and how to size and stop tests correctly.
• 🎓 A/B Testing at Scale (Tutorial) by the Experimentation Platform (exp-platform.com)
  • Tutorial slides and video lectures on running trustworthy controlled experiments at scale.
• 📚 Trustworthy Online Controlled Experiments: Chapter 1 by Ron Kohavi, Diane Tang and Ya Xu
  • The freely released opening chapter of the canonical book on A/B testing.
• 💡 Evan’s Awesome A/B Tools by Evan Miller
  • Free in-browser statistical calculators for planning and analysing A/B tests.

Spatial analysis

• 📚 Geocomputation with R by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
  • A comprehensive, hands-on introduction to geographic data analysis and mapping in R.
• 📚 Spatial Data Science by Edzer Pebesma and Roger Bivand
  • A modern treatment of spatial and spatiotemporal data analysis in R.
• 📚 Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny by Paula Moraga
  • Modelling and visualising geospatial health data with R-INLA and Shiny.

Data Science community groups

Python groups

• 👥 PyData Meetup Groups
  • Directory of local PyData meetup groups around the world.
• 👥 PyLadies by PyLadies
  • An international mentorship community for women and friends in the Python community.

R groups

• 👥 Directory of R User Groups by Jumping Rivers
  • A maintained directory of R user groups worldwide.
• 👥 Complete list of R-Ladies groups by R-Ladies Global.
  • A directory of R-Ladies chapters around the world.
• 👥 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 social data project for the R community, with a new dataset to explore and visualise each week.
• 👥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
  • A tidy-data approach to text mining and analysis in R.
• 🎓 Advanced NLP with SpaCy by Ines Montani
  • A free, interactive course on doing modern NLP in Python with spaCy.
• 📜 100 Must read papers in NLP by Masato Hagiwara
  • A curated reading list of foundational papers in natural language processing.
• 🎓 Stanford CS 124: From Languages to Information by Dan Jurafsky + Stanford’s introductory course on language, information and the basics of NLP.
• 📚 Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper.
  • The official book for doing natural language processing in Python with NLTK.
• 🎓 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
  • The standard, comprehensive textbook covering both classical and neural NLP.
• ▶️ BERT Research Series by Chris McCormick
  • A video series unpacking how the BERT language model works.

Special Topics

• ▶️ Structural Equation Modelling by Erin M. Buchanan
  • A video playlist introducing structural equation modelling.
• 📝 PyTorch Tutorials and Recipes by PyTorch
  • Official tutorials and recipes for deep learning with PyTorch.