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.