So much this. If I have to interview another junior-level DS who has a MNIST project in their github and still somehow can't manage fizzbuzz or a fibonacci function I'm probably going to take up religious asceticism.
EDIT: I said junior, but I meant Senior. We're talking people with PhD's who claim to have done extensive software engineering in previous roles.
My hypothesis is that good stats people probably don't visualize (Aphant) or visualize very specific types of data in a unique way. Without visualization, people tend to fall back to logical thinking - or emotional thinking, depending.
For example, I have a friend who can look at 2D seismic data and see what the underground formation looks like in his mind, in 3D.
It's good though because they raise some really valid points about the importance of intuition. I joke with my colleagues that all we're doing is encoding data as a hilbert space and slapping an algorithm on it, but that elides the fact that intuition like the child is talking about is important for knowing how to build that hilbert space.
The rest is some unsupervised stuff like k-means and PCA.
What would you like to see instead? (INB4 CNNs/RNNs other deep learning topics)