[Eridrus]

I'm curious what resources you found useful to learn stats modelling and what sorts of approaches have been useful.

On one hand, it's almost a tautoloy that specific models should be better than general models, but I worked on some 2d time series classification with a statistician and afterwards, for kicks, I replaced the entire thing with a CNN+LSTM and it worked just as well as the whole complicated model he had come up with.

[RA_Fisher]

I highly recommend this econometrics text for getting started with statistics: https://www.amazon.com/Principles-Econometrics-5th-Carter-Hi...

For modeling I found Wooldridge's panel and cross-section data book very useful: https://www.amazon.com/Econometric-Analysis-Cross-Section-Pa...

Greene is a really useful reference text: https://www.amazon.com/Econometric-Analysis-8th-William-Gree...

For advanced stats theory, I recommend Casella and Berger https://www.amazon.com/Statistical-Inference-George-Casella/...

Hope that helps!

The more specific a model can be made to the problem at hand, the better it'll perform. Supervised ML models are great starting / baseline models.

[tomrod]

I second Wooldridge. Greene I found to be much denser without providing much additional insight. It is a popular MS/PhD entry text though.

I add any of Ken Train's work to this mix, especially on estimating discrete choice theory.