This answer belies the reality that the knowledge needed to conduct actual data science, short of genuine R&D (which is itself at best 10% of data science posit

This answer belies the reality that the knowledge needed to conduct actual data science, short of genuine R&D (which is itself at best 10% of data science positions, if that), can be acquired by a sufficiently intelligent person within 6-12 months of applied training. We have developed tools to deliberately abstract away the complexity of the underlying math, and they work well. They work exceedingly well. I once took a semester-long class in data science where untrained, mathphobe business students were running various kinds of regression models on cleaned up data in WEKA (poor choice of software, yes) by the end of it. Most data scientists can and should treat the algorithms themselves as black boxes the same way software engineers treat R-B BSTs as black boxes, without necessarily having to know how they work off the top of their heads. I once worked with a Stanford "data scientist" at a top tech company who couldn't immediately recall Bayes' Rule. He wasn't stupid. His knowledge was just structured in a way that reflected the reality of his day-to-day; this is how it works for all of us. The primary skills needed are: munging, featurization, analysis (basic stats and then a few other things like ROC, etc), and perhaps most importantly (and the thing I see PhDs in particular chronically fail at) operationalization. You do not need to know heavy math to run a model over data, which is the maximum level of sophistication required for most applications of data science that generate real business value. I see similar foolishness in data science as I do blockchain, to be quite honest: people hype up and gravitate towards the cutting edge, while forgetting, ignoring, or blatantly obscuring the power of simple math on big data. I guess it's important that people have an inflated view of the complexity of what most data scientists are doing because having the role at least somewhat cloaked in mystique boosts salaries in the long run. Any argument that a reasonably intelligent person can't be trained to be a legitimately effective data scientist is a counterproductive lie.