[unabashedturtle]

As someone that's done a fair bit of hiring for engineer and data scientist positions, my outlook on these 'coding academies' is pretty grim.

If you want someone that can hack together a web app in rails/node.js/django/framework of your choice then that's fine. But the reality is most people going through these code academies tend to get a really poor foundation in both writing "good" and "fast" code. There's very little focus on algorithmic fundamentals.

But really, it's the academies that claim to spew out data scientists that I have a problem with. Every candidate I've interviewed that came from one of these knew how to run a regression, use R, and fit a variety of classifiers (eg; random forest, feed-forward ANN). But not one of them could answer really basic questions about sampling and experimental design (one of them asked: "multiple comparisons..? What's that?").

I've found that people that come in completely self-taught oddly enough don't have these issues. Many of them really do focus on the fundamentals first. It's easier to teach someone that understands experimental design and probability theory how to fit a few models in R than to teach probability theory to a kid that knows how to copy and paste from stackoverflow.