When learning something new, often available resources are lacking in some departments - whether they're missing information, poorly written, or tedious and dry. But occasionally, some content just stands out as above and beyond the rest, serving to not only make the learning process enjoyable but also to kindle interest in further exploration. What is that for you?

This could encompass everything from computer programming to literary criticism, and could be in the form of a website, book, video tutorial, or the like.

Not leaving the house much these days (due to social distancing and also insane heat in NYC right now) means I've got some time to kill that I'd like to spend productively.

I took MIT 6.00.2x: Introduction to Computational Thinking and Data Science a few years back when I was refreshing my Python skills. I think it's been updated a bit since then. It was a high quality course and I enjoyed it, though there are so many Python-related courses these days, I can't guarantee it's the best.

I'm currently taking:

1. Model Thinking on Coursera from the University of Michigan. I don't know where I saw this recommended (maybe on Tildes or Hacker News?) but it's quite good so far. Scott Page teaches about how to use various models (mental models, computational ones, etc.) for breaking down and analyzing various problems and systems. I've only just started but I quite like it.

2. Testing and Monitoring Machine Learning Model Deployments on Udemy. Taking this along with a few coworkers since it's relevant to what I do. Only just starting but appears to be quite good and works through a well-documented example project on Github.

I've also come across a few that seem like they might be good courses for the future:

• Bayesian Methods for Hackers
• Probalistic graphical models on Coursera (3-part sequence, not free)
• Computational probability and inference

Now your turn: what have you taken? What did you like or not like, and why? What do you want to take?