Who has not heard that Bayesian statistics are difficult, computationally slow, cannot scale-up to big data, the results are subjective; and we don't need it at all? Do we really need to learn a lot of math and a lot of classical statistics first before approaching Bayesian techniques. Why do the most popular books about Bayesian statistics have over 500 pages? Bayesian nightmare is real or myth? Someone once compared Bayesian approach to the kitchen of a Michelin star chef with high-quality chef knife, a stockpot and an expensive sautee pan; while Frequentism is like your ordinary kitchen, with banana slicers and pasta pots. People talk about Bayesianism and Frequentism as if they were two different religions. Does Bayes really put more burden on the data scientist to use her brain at the outset because Bayesianism is a religion for the brightest of the brightest?