[techwizrd]

I agree with this approach, and this is roughly the approach my own Statistics master's degree takes as well. It can be challenging to understand the finer points of likelihoods and posteriors (and the how to choose a prior) without serious mathematics that you're unlikely to have upon entering a graduate statistics degree.

Starting with applied probability and applied statistics (incl. regression, ANOVA, GLMs) allow you to solve problems and feel useful and engaged before being thrown into the mathematical rigor required of Bayesian statistics.