One crucial skill you will need is feature engineering. Formal methods for it aren’t typically in data science classes. Still, it’s worth understanding in order to build ML applications. Unfortunately, there aren't many available tools today, but I expect that to change this year.

Deep learning addresses it to some extent, but isn’t always the best choice if you don’t have image / text data (eg tabular datasets from databases, log files) or a lot of training examples.

I’m the developer of a library called Featuretools (https://github.com/Featuretools/featuretools) which is a good tool to know for automated feature engineering. Our demos are also a useful resource to learn using some interesting datasets and problems: https://www.featuretools.com/demos