As for deep neural networks, in my opinion they are just an ensemble of neural networks, and ensemble methods have been shown to produce best-in-class results almost regardless of the base learning algorithm.
Also the whole sport analytic prediction industry mostly uses linear regression... But next hype thing is medical data for them but they're super slow in adopting new thing. Industry seems boring if you're not into sport and into modeling in general not just linear regression.
Many of them don't know how to code. And the stat program of most of these universities doesn't require any real coding classes. The professor give you code templates and you tweak it to get the result. But the stat classes are really good to handle data.
Data science in general like Chapman Hill and UC Irvine seems like a really wide range of stuff from data analyst, a lil programming, data visualization, etc... I don't feel like it goes deep enough in what Statistic does imo.
Most are stuck with Tableau and SAS. Pharmacy companies uses SAS. Startup companies and tech companies uses Python and R.
I'm speaking from one data point sample, my current stat grad program. My undergrad is comp sci and I did a few years in the tech industry before coming back to grad school.
If you don't mind sharing details; why'd you choose your current grad program?
I'm curious what Fisher, Neyman, and Pearson would say about the current state of the field. Especially considering how often they and other statisticians disagreed with each other throughout the 20th century.