Pretty frequently (if you'll pardon the pun). Almost all papers are written using non-Bayesian methods, people expect results in non-Bayesian terms, etc.

Besides that: I decided years ago (~2009) that as appealing as Bayesian approaches were to me, I should study 'normal' statistics & data analysis first - so I understood them and why I didn't want to use them before I began studying Bayesian statistics. I didn't want to wind up in a situation where I was some sort of Bayesian fanatic who could tell you how to do a Bayesian analysis but couldn't explain what was wrong with the regular approach or why Bayesian approaches were better!

(I think I'm going to be switching gears relatively soon, though: I'm working with a track coach on modeling triple-jumping performance, and the smallness of the data suggests it'll be a natural fit for a multilevel model using informative priors, which I'll want to read Gelman's textbook on, and that should be a good jumping off point.)