If you're interested in econometrics, I highly recommend checking out Marc Bellemare's "Metrics Mondays" blog posts, which are full of useful, pragmatic advice for applying econometric methods to real-world data: http://marcfbellemare.com/wordpress/metrics-mondays.
If you're coming from an ML-focused approach to statistics, studying econometrics can be an interesting change of pace, because the focus is totally different. ML practicioners tend to be focused on prediction, while econometricians tend to focus on causal inference - utilizing pseudo-experimental variation within the data to estimate causal effects between variables. This turns out to be really hard to do correctly, and learning the pitfalls can make it easy to identify potential weaknesses in other research.
Most econometric work has historically been done in Stata, although it seems like both R and Python have been increasing in prominence a bit recently.