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https://github.com/alastairrushworth/free-data-science

🛠️ 🌐 Thematic list of high-quality data science resources
https://github.com/alastairrushworth/free-data-science
Last synced: 12 days ago
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🛠️ 🌐 Thematic list of high-quality data science resources
Host: GitHub
URL: https://github.com/alastairrushworth/free-data-science
Owner: alastairrushworth
Created: 2020-06-28T09:24:18.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2022-08-13T10:18:53.000Z (about 2 years ago)
Last Synced: 2024-10-15T21:45:20.019Z (27 days ago)
Language: R
Homepage:
Size: 220 KB
Stars: 284
Watchers: 19
Forks: 47
Open Issues: 1
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project

README

        ---

title: "Free data science resources"

output:

  github_document

---

## 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__

+ `r emo::ji('college')` = Course

+ `r emo::ji('memo')` = Tutorial or blog post

+ `r emo::ji('books')` = Book or book chapter

+ `r emo::ji('play button')` = Video or webinar

+ `r emo::ji('headphone')` = Podcast or audio recording

+ `r emo::ji('users')` = Community or user forum 

+ `r emo::ji('scroll')` = Journal or technical article

+ `r emo::ji('bulb')` = Cheat sheet

+ `r emo::ji('check')` = List

## Software & Programming

### Getting started with R

+ `r emo::ji('books')` [__Modern Dive: Getting Started__ by Chester Ismay and Albert Y. Kim](https://moderndive.netlify.app/1-getting-started.html).  

  + The very first of first steps.  Install R & RStudio and what to do after that.

+ `r emo::ji('memo')` [__RYouWithMe: Basic Basics__ by Lisa Williams, RLadies Sydney](https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/).  

  + Tour of RStudio, installing and using packages and getting data into RStudio.

+ `r emo::ji('college')` [__Teacups, Statistics and Giraffes__ by Hasse Walum and Desirée de Leon](https://tinystats.github.io/teacups-giraffes-and-statistics/).  

  + Accessible introduction to R and statistics with interactive coding exercises.

+ `r emo::ji('play button')` [__A Gentle Introduction to Tidy Statistics in R__ by Thomas Mock, RStudio](https://rstudio.com/resources/webinars/a-gentle-introduction-to-tidy-statistics-in-r/).  

  + Webinar covering exploratory data analysis, tidyverse, statistical testing and plotting.  

+ `r emo::ji('college')` [__The R Bootcamp__ by Ted Laderas and Jessica Minnier](https://r-bootcamp.netlify.app/).  

  + A tidyverse-centric interactive course for data manipulation, graphics, data reshaping, and statistical modelling.

+ `r emo::ji('college')` [__RStudio Primers__ by RStudio](https://rstudio.cloud/learn/primers/).  

  + Interactive tutorials from RStudio covering data manipulation, visualisation and programming with R.

+ `r emo::ji('college')` [__Swirl: Learn R, in R__ by Ismael Fernández, Nick Carchedi and Sean Kross](https://swirlstats.com/).  

  + Learn R with interactive courses in the console.

+ `r emo::ji('college')` [__Using R for Data Journalism__ by Andrew Ba Tran](http://learn.r-journalism.com/en/introduction/).  

  + Video supported intro course with emphasis on wrangling and visualisation.

+ `r emo::ji('books')` [__R for Data Science__ by Garrett Grolemund and Hadley Wickham](https://r4ds.had.co.nz/).  

  + Comprehensive guide to using R programming for data science workflows.

+ `r emo::ji('books')` [__Introduction to Data Science: Data Analysis and Prediction Algorithms with R__ by Rafael A. Irizarry](https://rafalab.github.io/dsbook/).  

  + Introduction to data science focused topics in R: visualisation, wrangling, prediction and workflow.

+ `r emo::ji('bulb')` [__Base R Cheat Sheet__ by  Mhairi McNeill](https://github.com/rstudio/cheatsheets/blob/main/base-r.pdf).  

  + Quick overview of basic R functionality.

### Advancing with R

+ `r emo::ji('books')` [__Tidynomicon - A Brief Introduction to R for People Who Count From Zero__ by Greg Wilson](http://tidynomicon.tech/).  

  + An introduction to R for Python users.

+ `r emo::ji('books')` [__Hands-on Programming with R__ by Garrett Grolemund](https://rstudio-education.github.io/hopr/).  

  + A friendly introduction to the R language for non-programmers.

+ `r emo::ji('books')` [__R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics__ by James (JD) Long, Paul Teetor](https://rc2e.com/).  

  + Recipes and worked examples for performing core tasks in R.

+ `r emo::ji('memo')` [__R package primer: a minimal tutorial__ by Karl Broman](https://kbroman.org/pkg_primer/).  

  + Overview of R packages development.

+ `r emo::ji('books')` [__R Packages__ by Hadley Wickham and Jennifer Bryan](https://r-pkgs.org/).  

  + Comprehensive guide to how R packages work and how to write your own.

+ `r emo::ji('books')` [__Efficient R programming__ by Colin Gillespie and Robin Lovelace](https://csgillespie.github.io/efficientR/).  

  + Comprehensive introduction to writing faster and more efficient R code.

+ `r emo::ji('books')` [__Advanced R__ by Hadley Wickham](https://adv-r.hadley.nz/).  

  + Get deeper into R programming fundamentals, object oriented and functional programming concepts and a lot more.  A must-read for experience R users!

+ `r emo::ji('play button')` [__RStudio Webinars__ by RStudio](https://rstudio.com/resources/webinars/).  

  + Recordings of past RStudio webinars covering a variety of R and data science content.  

+ `r emo::ji('books')` [__An Introduction to R__ by W. N. Venables, D. M. Smith and the R Core Team](https://cran.r-project.org/doc/manuals/R-intro.pdf).  

  + Introduction to R written by the R-Core team.  

+ `r emo::ji('books')` / `r emo::ji('college')` [__Data science for economists__ by Grant McDermott](https://github.com/uo-ec607/lectures#data-science-for-economists).  

  + Slides and code examples covering wide ranging introduction to data science in R.

+ `r emo::ji('books')` / `r emo::ji('college')` [__Big Data in Economics__ by Grant McDermott](https://github.com/uo-ec510-2020-spring/lectures#big-data-in-economics-ec-410510).  

  + Notes cover the use of R with shell, GitHub, web scraping, docker and cloud compute.

+ `r emo::ji('books')` [__Handling Strings with R__ by Gaston Sanchez and Chitra Venkatesh](https://www.gastonsanchez.com/r4strings/).  

  + Detailed introduction to strings, manipulation, regex and text wrangling.

+ `r emo::ji('play button')` [__R Package Development__ by John Muschelli](https://www.youtube.com/playlist?list=PLk3B5c8iCV-T4LM0mwEyWIunIunLyEjqM).  

  + 6-part video series on the basics of R package development, testing and building a `pkgdown` site.

### Getting started with Python

+ `r emo::ji('memo')` [__Install Python and Anaconda__ by Anaconda](https://docs.anaconda.com/anaconda/install/).

  + The most commonly used package and environment manager for Python and how to install it.

+ `r emo::ji('college')` [__Free interactive introduction to Python and pandas__ by ?](https://python-course.nixd.dk/).  

  + Beginners introduction to Python, pandas and data analysis via an interactive course.

+ `r emo::ji('memo')` [__Quick reference to Python in a single script and notebook__ by Kevin Markham](https://www.dataschool.io/python-quick-reference/).

  + Comprehensive reference guides for Python programming via notebooks and script examples.

+ `r emo::ji('college')` / `r emo::ji('play button')` [__An Introduction to Python and Programming__ by Alexander Hess](https://github.com/webartifex/intro-to-python).

  + Python course for aspiring data scientists via notebooks, videos and exercises.

+ `r emo::ji('books')`[__A Whirlwind Tour of Python__ by Jake VanderPlas](https://jakevdp.github.io/WhirlwindTourOfPython/index.html).

  + A fast-paced introduction to essential features of the Python language for those already familiar with another language.

+ `r emo::ji('college')`  [__Learn Python__ by Ron Reiter](https://www.learnpython.org/).

  + Interactive online courses and tutorials for a wide range of Python topics.

+ `r emo::ji('bulb')` [__Pandas Cheat Sheet__ by the Pandas development team](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf).

  + 2-page quick reference to the most commonly used `pandas` functions. 

+  `r emo::ji('memo')` [__Getting Started in pandas__ by the Pandas development team](https://pandas.pydata.org/pandas-docs/stable/getting_started/index.html#getting-started).

  + Tutorials and quick start guides from the `pandas` development team.

### Advancing with Python

+ `r emo::ji('books')` [__Python Data Science Handbook__ by Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/).

  + Online book with comprehensive coverage of IPython, numpy, pandas, matplotlib and machine learning with scikit-learn.

+ `r emo::ji('books')` [__Python for Everybody: Exploring Data Using Python 3__ by Charles R. Severance](https://www.py4e.com/book.php).

  + Python ebook with a focus on programming fundamentals.  Translations available in several languages.

+ `r emo::ji('memo')` [__Python Packaging User Guide__ by the Python Packaging Authority (PyPA)](https://packaging.python.org/).

  + A collection of tutorials and references to help you distribute and install Python packages with modern tools.

### Shell

+ `r emo::ji('college')` [__Learn Shell__ by Ron Reiter](https://www.learnshell.org/).

  + A browser-based interactive Shell tutorial covering basics through to advanced topics.

+ `r emo::ji('college')` [__The Unix Shell__ by Software Carpentry](http://swcarpentry.github.io/shell-novice/).

  + Tutorials and examples of how to use the unix shell.

+ `r emo::ji('memo')` [__Beginners/BashScripting__ by Ubuntu Documentation](https://help.ubuntu.com/community/Beginners/BashScripting).

  + Introduction to using the shell for OS navigation and scripting.

+ `r emo::ji('play button')` [__How to Write a Shell Script using Bash Shell in Ubuntu__ by FS Tutorial](https://www.youtube.com/watch?v=He-5BpUGSag)

  + Short video showing how to write a first shell script using vim.

+ `r emo::ji('college')` / `r emo::ji('play button')` [__The Missing Semester of Your CS Education__ by Anish Athalye, Jon Gjengset and Jose Javier Gonzalez Ortiz](https://missing.csail.mit.edu/) 

  + Videos and notes on using shell and version control.

+ `r emo::ji('memo')` [__The Art of the Command Line__ by Joshua Levy](https://github.com/jlevy/the-art-of-command-line)

  + Useful list of bash commands and explanations, all laid out on a single page!

+ `r emo::ji('memo')` [__ExplainShell.com__ by Idan Kamara](https://explainshell.com/)

  + Handy utility - type in a shell command and get an explanation of what it does.

### Regular expressions

+ `r emo::ji('college')` [__RegexOne: Learn Regular Expressions with simple, interactive exercises.__ by RegexOne](https://regexone.com/)

  + Simple, browser based course with interactive exercises.

+ `r emo::ji('memo')` [__Regular Expressions 101: Online Regular Expression Tester and Debugger__ by Firas Dib](https://regex101.com/)

  + Very handy tool to test regular expressions against test strings. 

+ `r emo::ji('bulb')` [__Data Science Cheat Sheet: Python Regular Expressions__ by Dataquest](https://www.dataquest.io/wp-content/uploads/2019/03/python-regular-expressions-cheat-sheet.pdf)

  + PDF cheat-sheet for standard regular expression syntax.

+ `r emo::ji('bulb')`[__Regular Expressions Cheat Sheet__ by Dave Child](https://cheatography.com/davechild/cheat-sheets/regular-expressions/pdf/)

  + PDF cheat-sheet for standard regular expression syntax.

### Git 

+ `r emo::ji('books')` [__Happy Git and GitHub for the useR__ by Jenny Bryan, the STAT 545 TAs and Jim Hester](https://happygitwithr.com/) 

  + If you are an R user and new to git, this is currently the best place to start.

+ `r emo::ji('memo')` [__An introduction to Git and how to use it with RStudio__ by François Michonneau](https://r-bio.github.io/intro-git-rstudio/)

  + Conceptual overview of what git is and how to use it, with particular emphasis on Github and its use with RStudio.

+ `r emo::ji('bulb')` [__Git Cheat Sheet__ by GitHub](https://github.github.com/training-kit/downloads/github-git-cheat-sheet.pdf)

  + A list of the main git shell commands.

+ `r emo::ji('books')` [__Pro Git__ by Scott Chacon and Ben Straub](https://git-scm.com/book/en/v2)

  + Free ebook covering more advanced usage of git - good once you're confident with the basics.

+ `r emo::ji('memo')` [__Oh Shit Git!__ by Katie Sylor-Miller](https://ohshitgit.com)

  + Light-hearted troubleshooting guide for when things inevitably go wrong!

+ `r emo::ji('memo')` [__Step-by-step guide to contributing on GitHub__ by Kevin Markham](https://www.dataschool.io/how-to-contribute-on-github/)

  + Detailed guide on how to contribute to open source software projects using git and Github.

### Spark 

+ `r emo::ji('bulb')` [__PySpark Cheat Sheet__ by Kevin Schaich](https://github.com/kevinschaich/pyspark-cheatsheet)

+ `r emo::ji('college')` [__Mastering Spark with R__ by Javier Luraschi, Kevin Kuo and Edgar Ruiz](https://therinspark.com/)

+ `r emo::ji('play button')` [__R & Spark: How to Analyze Data Using RStudio's Sparklyr__ by Nathan Stephens](https://www.youtube.com/watch?v=oItFZfzqqMY)

+ `r emo::ji('books')`[__A Gentle Introduction to Spark__ by DataBricks](http://www.dcs.bbk.ac.uk/~dell/teaching/cc/book/databricks/spark-intro.pdf)

### SQL

+  `r emo::ji('books')` / `r emo::ji('college')` [__The SQL Tutorial for Data Analysis__ by mode.com](https://mode.com/sql-tutorial/introduction-to-sql/).  Tutorials and interactive exercises teaching fundamentals of SQL.

+ `r emo::ji('college')` [__SQLBolt: Learn SQL with simple, interactive exercises__](https://sqlbolt.com/).

+ `r emo::ji('books')` / `r emo::ji('college')` [__SQLZoo: SQL Tutorial__](https://sqlzoo.net/).  Wikibook with interactive exercises.

+ `r emo::ji('college')` [__Intro to SQL: Querying and managing data__ by Khan Academy](https://www.khanacademy.org/computing/computer-programming/sql)

+ `r emo::ji('college')` [__LearnSQLOnline__ by Ron Reiter](https://www.learnsqlonline.org/)

### Docker

+ `r emo::ji('memo')` [__An Introduction to Docker for R Users__ by Colin Fay](https://colinfay.me/docker-r-reproducibility/)

+ `r emo::ji('college')` [__R Docker tutorial__ by Jemma Stachelek](https://jsta.github.io/r-docker-tutorial/)

+ `r emo::ji('play button')` [__Docker and Python: making them play nicely and securely for Data Science and ML__ by Tania Allard at PyCon 2020](https://us.pycon.org/2020/schedule/presentation/175/)

### Markdown, LaTeX and publishing

+ `r emo::ji('books')` [__R Markdown: The Definitive Guide__ by Yihui Xie, J. J. Allaire, Garrett Grolemund](https://bookdown.org/yihui/rmarkdown/)

+ `r emo::ji('books')` [__bookdown: Authoring Books and Technical Documents with R Markdown__ by Yihui Xie](https://bookdown.org/yihui/bookdown/)

+ `r emo::ji('books')` [__The Not So Short Introduction to LaTeX 2ε__ by Tobias Oetiker](https://tobi.oetiker.ch/lshort/lshort.pdf)

+ `r emo::ji('books')` [__LaTeX for Beginners__ by UoE IS Services](https://www.colorado.edu/aps/sites/default/files/attached-files/latex_primer.pdf)

## Machine Learning

### Theory

+ `r emo::ji('books')` [__The Elements of Statistical Learning: Data Mining, Inference, and Prediction__ by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2017)](https://web.stanford.edu/~hastie/ElemStatLearn/download.html)

+ `r emo::ji('books')` [__Computer Age Statistical Inference: Algorithms, Evidence and Data Science__ by Bradley Efron and Trevor Hastie (2017).](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)

  + A statistical approach to data science and machine learning.

+ `r emo::ji('books')`[__Mathematics for Machine Learning__ by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong](https://mml-book.github.io/book/mml-book.pdf)

  + Covers the underpinning theory to many ML algorithms, a useful reference for practitioners.

+ `r emo::ji('memo')` [__distill.pub__ by multiple contributors, edited by Shan Carter and Chris Olah](https://distill.pub/)

  + Online scientific journal publishing very high-quality, interactive articles on ML.  On hiatus as of 2021.

+ `r emo::ji('books')` [__Mining of Massive Datasets__ by Jure Leskovec, Anand Rajaraman, Jeff Ullman](http://www.mmds.org/)

  + Book based on Stanford Computer Science course [CS246: Mining Massive Datasets](http://web.stanford.edu/class/cs246/).

+ `r emo::ji('books')` [__Introduction to Statistical Learning__ by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani](https://hastie.su.domains/ISLR2/ISLRv2_website.pdf)

  + ISLR is still one of the most important books for getting started in practical ML.

### Interpretability 

+ `r emo::ji('books')` [__Interpretable Machine Learning: A Guide for Making Black Box Models Explainable__ by Christoph Molnar (2022)](https://christophm.github.io/interpretable-ml-book/)

  + A highly practical introduction to IML, required reading if you are new to the topic.

+ `r emo::ji('check')` [__Awesome: Machine Learning Interpretability__ by Patrick Hall ](https://github.com/jphall663/awesome-machine-learning-interpretability)

  + A big list of MLI resources with >2.5k github stars.

### Guides, tutorials and courses

+ `r emo::ji('college')` [__Machine Learning Crash Course with TensorFlow APIs__ by Google](https://developers.google.com/machine-learning/crash-course)

  + fast-paced, practical introduction to machine learning, with video lectures, real-world case studies, and hands-on practice exercises.

+ `r emo::ji('memo')` [__Tidymodels Tutorials__ by RStudio](https://www.tidymodels.org/learn/)

  + Variety to beginners guides to solving common ML tasks with R's tidymodels.

+ `r emo::ji('college')` [__Supervised Machine Learning Case Studies in R__ by Julia Silge.](https://supervised-ml-course.netlify.app/)

  + Easy-to-follow in-browser beginner's guide to using R's tidymodels for practical ML.

+ `r emo::ji('memo')` / `r emo::ji('play')` [__Introduction to machine learning with scikit-learn__ by Justin Markham](https://github.com/justmarkham/scikit-learn-videos)

  + Bite size study videos and python notebooks by Justin Markham's Data School.

+ `r emo::ji('memo')` [__scikit-learn User Guide__ by scikit-learn](https://scikit-learn.org/stable/user_guide.html)

  + sci-kit learn's documentation are very thorough and a great standalone learning resource!

+ `r emo::ji('college')` [__Introduction to Machine Learning for Coders__ by Jeremy Howard.](http://course18.fast.ai/ml)

  + 24 hours of videos and supporting notes from a Kaggle superstar.

## Data Science Practice

### Software development 

+ `r emo::ji('memo')` [__Software development skills for data scientists__ by Trey Causey](http://treycausey.com/software_dev_skills.html)

+ `r emo::ji('scroll')` [__Hidden Technical Debt in Machine Learning Systems__](https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf)

+ `r emo::ji('memo')` [__How rOpenSci uses Code Review to Promote Reproducible Science__ by Noam Ross, Scott Chamberlain, Karthik Ram and Maëlle Salmon](https://ropensci.org/blog/2017/09/01/nf-softwarereview/)

+ `r emo::ji('scroll')`  [__Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research__ by Victoria Stodden and Sheila Miguez](https://openresearchsoftware.metajnl.com/articles/10.5334/jors.ay/)

+ `r emo::ji('memo')` [__Journalism as a Professional Model for Data Science__ by Brian C. Keegan](https://www.brianckeegan.com/2016/02/journalism-as-a-professional-model-for-data-science/)

+ `r emo::ji('memo')` [__Cookiecutter Data Science__ by drivendata](https://github.com/drivendata/cookiecutter-data-science)

### Hiring and building teams

+ `r emo::ji('books')` [__The Care and Feeding of Data Scientists: How to Build, Manage and Retain a Data Science Team__ by Michelangelo D'Agostino and Katie Malone](https://oreilly-ds-report.s3.amazonaws.com/Care_and_Feeding_of_Data_Scientists.pdf)

+ `r emo::ji('headphone')` [__The Care and Feeding of Data Scientists: Becoming a Data Science Manager__ on Linear Digressions podcast by Katie Malone and Ben Jaffe](http://lineardigressions.com/episodes/2019/10/18/the-care-and-feeding-of-data-scientists-becoming-a-data-science-manager)

+ `r emo::ji('memo')` [__Models for integrating data science teams within companies__ by Pardis Noorzad](https://djpardis.medium.com/models-for-integrating-data-science-teams-within-organizations-7c5afa032ebd)

+ `r emo::ji('headphone')` [__Building Effective Data Science Teams__ with Kobi Abayomi, Gregory Berg, Elaine McVey, Jacqueline Nolis, Nasir Uddin and Julia Silge](https://www.rstudio.com/resources/webinars/building-effective-data-science-teams/)

+ `r emo::ji('memo')` [__Building a data team at a mid-stage startup: a short story__ by Erik Bernhardsson](https://erikbern.com/2021/07/07/the-data-team-a-short-story.html)

+  `r emo::ji('memo')` [__Hiring a data scientist__ by Mikhail Popov, Wikimedia](https://diff.wikimedia.org/2017/02/02/hiring-data-scientist/)

### Agile data science 

+ `r emo::ji('books')` [__Agile Data Science with R: A workflow__ by Edwin Thoen](https://edwinth.github.io/ADSwR/)

+ `r emo::ji('memo')` [__Data Science and Agile (What works, and what doesn't)__ by Eugene Yan](https://eugeneyan.com/writing/data-science-and-agile-what-works-and-what-doesnt/)

+ `r emo::ji('memo')` [__Data Science Best Practices: Run your data science team like an engineering team__ by Leonard Austin](https://syslog.ravelin.com/data-science-best-practices-843c9693db8)

+ `r emo::ji('memo')` [__Organizing machine learning projects: project management guidelines__ by Jeremy Jordan](https://www.jeremyjordan.me/ml-projects-guide/)

### Ethics and fairness

+ `r emo::ji('books')` [__Ethics of Artificial Intelligence and Robotics__ by Stanford Encyclopedia of Philosophy](https://plato.stanford.edu/entries/ethics-ai/)

+ `r emo::ji('memo')` [__The Responsible Machine Learning Principles: A practical framework to develop AI responsibly__ by The Institute for Ethical AI & Machine Learning](https://ethical.institute/principles.html)

+ `r emo::ji('memo')` [__A Code of Ethics for Data Science__ by DJ Patil](https://medium.com/@dpatil/a-code-of-ethics-for-data-science-cda27d1fac1)

+ `r emo::ji('memo')` [__The Ethical Data Scientist__ by Cathy O' Neil](https://slate.com/technology/2016/02/how-to-bring-better-ethics-to-data-science.html)

+ `r emo::ji('memo')` [__An ethics checklist for data scientists__ by drivendata](https://deon.drivendata.org/)

+ `r emo::ji('books')` [__Fairness and machine learning: Limitations and Opportunities__ by Solon Barocas, Moritz Hardt, Arvind Narayanan](https://fairmlbook.org/)

+ `r emo::ji('memo')` [__Practical Data Ethics__ by fast.ai](https://ethics.fast.ai/)

### MLOps

+ `r emo::ji('memo')` [__MLOps: Continuous delivery and automation pipelines in machine learning__ by Google Cloud](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)

+ `r emo::ji('memo')` [__Using GitHub Actions for MLOps & Data Science__ by Hamel Husain, The Github Blog](https://github.blog/2020-06-17-using-github-actions-for-mlops-data-science/)

+ `r emo::ji('memo')` [__Continuous Delivery for Machine Learning: Automating the end-to-end lifecycle of Machine Learning applications__ by Danilo Sato, Arif Wider and Christoph Windheuser](https://martinfowler.com/articles/cd4ml.html)

+ `r emo::ji('memo')` [__Monitoring Machine Learning Models in Production: A Comprehensive Guide__ by Christopher Samiullah](https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/)

+ `r emo::ji('memo')` [__What are Azure Machine Learning pipelines?__ by Microsoft](https://docs.microsoft.com/en-gb/azure/machine-learning/concept-ml-pipelines)

+ `r emo::ji('memo')` [__Getting started with Kubeflow Pipelines__ by Amy Unruh, Google Cloud](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines)

+ `r emo::ji('memo')` [__Continuous Machine Learning (CML) is CI/CD for Machine Learning Projects__ by DVC.org](https://cml.dev/)

+ `r emo::ji('memo')` [__Data Science Workflows__ by David Neuzerling](https://mdneuzerling.com/post/data-science-workflows/)

+ `r emo::ji('memo')` [__Monitoring Machine Learning Models in Production A Comprehensive Guide__ by Christopher Samiullah](https://christophergs.com/machine%20learning/2020/03/14/how-to-monitor-machine-learning-models/)

### ML Platforms

+ `r emo::ji('memo')` [__The problem with AI developer tools for enterprises (and what IKEA has to do with it)__ by Clemens Mewald](https://towardsdatascience.com/the-problem-with-ai-developer-tools-for-enterprises-and-what-ikea-has-to-do-with-it-b26277841661)

+ `r emo::ji('memo')` [__5 Reasons Organizations Shouldn’t Build Their Own AI Platforms__ by dataiku](https://blog.dataiku.com/5-reasons-organizations-shouldnt-build-their-own-ai-platforms)

### Style Guides

+ `r emo::ji('memo')` [__Udacity Git Commit Message Style Guide__ by Udacity](http://udacity.github.io/git-styleguide/)

+ `r emo::ji('books')` [__The tidyverse style guide__ by Hadley Wickham](https://style.tidyverse.org/)

+ `r emo::ji('memo')`[__The Google R Style Guide__ by Google](https://google.github.io/styleguide/Rguide.html)

+ `r emo::ji('memo')` [__The Google Python Style Guide__ by Google](https://google.github.io/styleguide/pyguide.html)

+ `r emo::ji('memo')` [__PEP 8 -- Style Guide for Python Code__ by Guido van Rossum, Barry Warsaw, Nick Coghlan](https://peps.python.org/pep-0008/)

## Developing interactive applications

+ `r emo::ji('play')` / `r emo::ji('college')` [__Learn Shiny__ by RStudio](https://shiny.rstudio.com/tutorial/)

+ `r emo::ji('books')` [__A gRadual intRoduction to Shiny__ by Ted Laderas and Jessica Minnier](https://laderast.github.io/gradual_shiny/)

+ `r emo::ji('books')` [__Interactive web-based data visualization with R, plotly, and shiny__ by Carson Sievert](https://plotly-r.com/)

+ `r emo::ji('books')` [__Dashboards__ by Yihui Xie, J. J. Allaire, Garrett Grolemund](https://bookdown.org/yihui/rmarkdown/dashboards.html).  Chapter 5 from 'R Markdown: The Definitive Guide'.

+ `r emo::ji('memo')` [__Leaflet for R__ by RStudio](https://rstudio.github.io/leaflet/)

+ `r emo::ji('memo')` [__Dash User Guide__ by Plotly](https://dash.plotly.com)

+ `r emo::ji('memo')` [__Getting Started with Streamlit__ by streamlit](https://docs.streamlit.io/library/get-started)

## Visualisation

+ `r emo::ji('books')` [__Fundamentals of Data Visualization__ by Claus O. Wilke](https://serialmentor.com/dataviz/)

+ `r emo::ji('books')` [__ggplot2: Elegant Graphics for Data Analysis__ by Hadley Wickham](https://ggplot2-book.org/)

+ `r emo::ji('memo')` [__3D Mapping and Visualization with R and Rayshader__ by Tyler Morgan-Wall](https://github.com/tylermorganwall/MusaMasterclass)

## Time series analysis

+ `r emo::ji('books')` [__Forecasting: Principles and Practice__ by Rob J Hyndman and George Athanasopoulos](https://otexts.com/fpp2/)

+ `r emo::ji('memo')` [__11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)__ by Jason Brownlee](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/)

## Generalised Additive Modelling (GAMs)

+ `r emo::ji('college')` [__GAMs in R__ by Noam Ross](https://noamross.github.io/gams-in-r-course/)  Interactive course introducing Generalised Additive Models (GAMs).

+ `r emo::ji('memo')` [__Resources for Learning About and Using GAMs in R__ by Noam Ross](https://github.com/noamross/gam-resources)

## Statistics

+ `r emo::ji('books')` [__Statistical Inference via Data Science: A Modern Dive into R and the tidyverse__ by Chester Ismay and Albert Y. Kim](https://moderndive.com/)

+ `r emo::ji('books')` [__Think Stats Exploratory Data Analysis in Python__ by Allen B. Downey](http://greenteapress.com/thinkstats2/thinkstats2.pdf)

+ `r emo::ji('books')` [__Learning statistics with R: A tutorial for psychology students and other beginners__ Danielle Navarro](https://learningstatisticswithr.com/book/)

+  `r emo::ji('books')` [__Probabilistic Programming & Bayesian Methods for Hackers__ by Cameron Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)

+ `r emo::ji('books')` [__From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science__ by Norm Matloff](http://heather.cs.ucdavis.edu/~matloff/132/PLN/probstatbook/ProbStatBook.pdf)

+ `r emo::ji('books')` [__Theory of Statistics__ by James E. Gentle](http://mason.gmu.edu/~jgentle/books/MathStat.pdf)

+ `r emo::ji('books')` [__Core Statistics__ by Simon Wood](https://www.maths.ed.ac.uk/~swood34/core-statistics.pdf)

## Spatial analysis 

+ `r emo::ji('books')` [__Geocomputation with R__ by Robin Lovelace, Jakub Nowosad, Jannes Muenchow](https://geocompr.robinlovelace.net/)

+ `r emo::ji('books')` [__Spatial Data Science__ by Edzer Pebesma and Roger Bivand](https://keen-swartz-3146c4.netlify.app/)

+ `r emo::ji('books')` [__Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny__ by Paula Moraga](https://www.paulamoraga.com/book-geospatial/)

## Data Science community groups

### Python groups

+ `r emo::ji('users')` [__PyData Meetup Groups__](https://www.meetup.com/pro/pydata/)

+ `r emo::ji('users')` [__PyLadies__ by PyLadies](https://www.pyladies.com/)

### R groups

+ `r emo::ji('users')` [__Directory of R User Groups__ by Jumping Rivers](https://jumpingrivers.github.io/meetingsR/r-user-groups.html)

+ `r emo::ji('users')` [__Complete list of R-Ladies groups__ by R-Ladies Global](https://benubah.github.io/r-community-explorer/rladies.html).  

+ `r emo::ji('users')` [__R for Data Science Online Learning Community__](https://www.rfordatasci.com/)

   + The R4DS Online Learning Community is a community of R learners at all skill levels working together to improve their skills.

+ `r emo::ji('users')` [__Tidy Tuesday__](https://www.tidytuesday.com/) 

  + A weekly podcast and community activity brought to you by the R4DS Online Learning Community.

+ `r emo::ji('users')`[__SatRdays__ SatRdays](https://satrdays.org/)

  +R-focused conferences that are held on Saturdays.

## Natural language processing

  + `r emo::ji('books')` [__Text Mining with R: A Tidy Approach__ by Julia Silge and David Robinson](https://www.tidytextmining.com/)

  + `r emo::ji('college')` [__Advanced NLP with SpaCy__ by Ines Montani](https://course.spacy.io/en/)

  + `r emo::ji('scroll')` [__100 Must read papers in NLP__ by Masato Hagiwara](https://github.com/mhagiwara/100-nlp-papers)

  +  `r emo::ji('college')` [__Stanford CS 124: From Languages to Information__ by Dan Jurafsky](http://web.stanford.edu/class/cs124/)

  + `r emo::ji('books')` [__Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit__ by Steven Bird, Ewan Klein, and Edward Loper.](http://www.nltk.org/book/)

  + `r emo::ji('college')`  [__A Code-First Intro to Natural Language Processing__ by fast.ai](https://github.com/fastai/course-nlp)

  + The course is taught in Python with Jupyter Notebooks, using libraries such as sklearn, nltk, pytorch, and fastai.

+ `r emo::ji('books')` [__Speech and Language Processing__ by Dan Jurafsky and James H. Martin](https://web.stanford.edu/~jurafsky/slp3/)

+ `r emo::ji('play button')` [__BERT Research Series__ by Chris McCormick](https://www.youtube.com/playlist?list=PLam9sigHPGwOBuH4_4fr-XvDbe5uneaf6)

## Special Topics

+ `r emo::ji('play')` [__Structural Equation Modelling__ by Erin M. Buchanan](https://www.youtube.com/playlist?list=PLw93TUuxrFAZkJVc5dhgTZpOT7qmTjlT7&app=desktop)

+ `r emo::ji('memo')` [__PyTorch Tutorials and Recipes__ by PyTorch](https://pytorch.org/tutorials/)