Although I started with Google’s “Crash Course in Python”, I’d recommend this course as a first foray into the topic. It sets you up with many of the key tools you need to start exploring and version-controlling your work, including the applications you’ll need to learn Python and R.

This John Hopkins University course is included with Coursera Plus, which alongside my water bill and cat are probably the best value for money items in my life (both my wonderful husband, and the Economist magazine represent exceptional value as well, but come with a significantly higher outlay).

The course is a little older and offers an automated voice-over, but this is excellent for listening on double speed. Week 1 covers general definitions, processes, and troubleshooting methods – nothing particularly groundbreaking but good to start with and easy to get through. Week 2’s exploration of R and RStudio is well-paced and insightful. It really helped set things up, and I wish there were a Jupyter Labs version of this (maybe there is, but I haven’t found it yet). It’s still quick, easy, and intuitive to get through, and I encounter no bugs.

Week 3 is significantly more intense, and either the course is outdated or somehow my setup isn’t quite right. It takes me quite some time to resolve the issues around the SNV directory, and my tutor guides me through downloading VisualSVN Server. We both feel like this should have been more straightforward, but solve it and move on.

Markdown in week 4 also presents opportunities to troubleshoot when RStudio returns error messages to let me know that Latex hasn’t been installed. This isn’t just a simple pip install and takes some time to resolve; this time by installing basic-miktex-23.4-x64. Finally, I knit together my first R markdown into PDF and move on.

We then delve into a cursory but refreshing overview of stats techniques and move on to experimental design. More detail would have been welcome, but the course does offer a few amusing links and comments. I loved the Fivethirtyeight playground designed to allow punters to hack their way to a desired p-value. Might sound awful to academics, but after 20 years of requests to cherry-pick data for executive presentations, I just have to smile.

Finally, the course finishes off with a peer graded assesment. First two items are ludicrously easy – take a screenshot to show you’ve downloaded and installed RStudio and R, and then create a gibhub repo. Third requests you to push a local text file to the github repository. It takes a while to get Gitbash to cooperate. Note to others: never put spaces between your file or folder names (and don’t forget the double apostrophe “”). Last stop is to fork a data sharingrepository and share the link. Finally, students need to grade 3 other student submissions. Hopefully mine will be done soon, and I’ll be able to sign off my first course!