There was a similar thread awhile back. Aside from the materials already suggested, I think Philip I Good’s books from a resampling perspective would be a good place to start for those without the strongest mathematical background.
The material should be explored further from a decision theory perspective that links Bayesian with Frequentist ideas. The closest book that does this is (but is somewhat math heavy):
If there isn’t all that much time, the following papers are worth study, in that you can use your frequentist toolkit as procedures to generate inputs for a broader Bayesian perspective.
https://www.sciencedirect.com/science/article/pii/S002224961600002X
https://projecteuclid.org/journals/statistical-science/volume-24/issue-2/Relaxation-Penalties-and-Priors-for-Plausible-Modeling-of-Nonidentified-Bias/10.1214/09-STS291.full
Greenland, S. (2005). Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168: 267-306. link
Greenland, S. (2000), When Should Epidemiologic Regressions Use Random Coefficients?. Biometrics, 56: 915-921. https://doi.org/10.1111/j.0006-341X.2000.00915.x