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https://github.com/nrennie/data-science-resources

Resources relating to data science.
https://github.com/nrennie/data-science-resources

data-science resources

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Resources relating to data science.

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# Data Science Resources

See resources at: [nrennie.rbind.io/data-science-resources](https://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.