I think I like this distinction between Bayesian and Frequentist statistics:
“we are nearly always ultimately curious about the Bayesian probability of the hypothesis (i.e. “how probable it is that things work a certain way, given what we see”) rather then in the frequentist pobability of the data (i.e. “how likely it is that we would see this if we repeated the experiment again and again and again”).”
But I think the rest of the article gives a mischaracterization of data science, take for instance the following paragraph:
“But most importantly, data-driven science is less intellectually demanding then hypothesis-driven science. Data mining is sweet, anyone can do it. Plotting multivariate data, maps, “relationships” and colorful visualizations is hip and catchy, everybody can understand it. By contrary, thinking about theory can be pain and it requires a rare commodity: imagination.”
Actually, in my opinion it takes way more imagination to develop an effective data visualization than develop an estimator by hand and prove its unbiased or consistent. I’d much rather do the latter because I frankly don’t have the imagination to do the best job with the former.
But, data science is much more than visualization. As far as not being intellectually demanding, trying to understand the back proposition algorithm used by neural networks not to mention actually coding your own algorithm isn’t child’s play.
As far as results, ultimately it is about getting the right tool for the right job. There are plenty of cases, in bioinformatics and genomics for example where the algorithmic approach is more useful than say ANOVA. As Leo Brieman said:
“Approaching problems by looking for a data model imposes an apriori straight jacket that restricts the ability of statisticians to deal with a wide range of statistical problems.”