Data Science Resources

See resources at: nrennie.rbind.io/data-science-resources.

Adding or editing a resource

• Please make a pull request with an edit to the resources.csv file.
• Ensure all four columns are complete.
  • Type: valid options are Blog, Book, Newsletter, Website, Community, Data, Challenge, Video, or Podcast. If a resource fits into multiple Type categories, please choose the one that fits best.
  • Name: the name of the resource e.g. Who's blog is it? Or what is the name of the book?
  • Link: the full URL to the resource. Links should be to the main resource page e.g. link to a blog rather than a single blog post, or a YouTube channel rather than a single video.
  • Category: the category of the resource e.g. the programming language, or area of data science covered. Resources may have multiple categories, which should be separated by a semi-colon, ;. Current categories include R, Python, Julia, Data, JavaScript, Rust, Quarto, Git, Shiny, Forecasting, Time Series, Data Visualisation, Machine Learning, Statistics, Programming, Community, Teaching and Data Science. If you are adding a new category, please explain why in your PR description.