I wonder if anyone could actually learn the basics of Big Oh notation from this. My students struggle with the concept, and I couldn't imagine this being sufficient for helping them get a deep or even cursory understanding.
Like, once you write a few functions you sort of just intuitively know the O runtime of the algorithm. Once you store a few things in memory, you intuitively know the O space of the storage. And when it breaks, you look for somewhere that you introduced n^2 and fix it.
Unless you're optimizing search algorithms, it almost never matters in practice. Even that double nested loop won't matter until you have a big dataset.