Here you will find all the course materials for the "Stats from Scratch" stream of our Data Science course, e.g. the datasets used in the tutorials, and occasionally some starter scripts.

Each tutorial has a link to a GitHub repository that contains the data needed to work through the code examples. If you are only planning on doing a few tutorials from this stream, you may choose to download only specific materials, but otherwise, we suggest you download this whole repository in one go, and therefore you will not have to download data folders separately each time.

• Click on the green button "Clone or Download" in the top right corner of the screen.
• Download the repository as a ZIP file, and save it preferably somewhere close to the root of your computer (e.g. C:/CC_course_stream1). Avoid saving it in a folder that has spaces of special characters in its name.
• All the data is now on your computer!

For even better ease of working, you can create a Project in RStudio that will be associated with the Stream folder.

• In RStudio, go to File / New Project / Create from existing directory and navigate to your folder.
• Create the project. You should see the name of your project appear in the top right corner.
• Now, instead of setting a working directory in each script, you can use relative paths to load your data. This is good practice because you could share the whole folder with someone else, and as long as they create a project in the main Stream folder too, they will be able to run your code without changing anything.

You will notice that the tutorial folders have been numbered; this is only a suggestion of a logical, progressive order for this stream, but you don't have to follow it!

The online tutorials that are currently part of this stream are:

• Getting started with R & RStudio
• Troubleshooting and how to find help
• Coding Etiquette
• Basic data manipulation
• Intro to linear models
• Data visualisation I

This course is created by the Coding Club with support from the University of Edinburgh and the Data Lab .

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.