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One thing I want to emphasize: ease-of-use is obviously beneficial to those with less expertise, who go from 0 to 1. But the people who gain the most from it are actually the experts, who are now able to dream bigger and move faster. Experts go from 1 to 10.
"My expertise is devalued, undergrads can reimplement my PhD thesis in 30 minutes 
" is the wrong mindset.
It's actually: "my expertise is being multiplied, I can now achieve dramatically more ambitious milestones while leveraging more enjoyable and productive workflows 
"
"Oh no, better tools! They're helping the kids catch up to me!" vs "Yay, better tools! Can't wait to see how far I can go with them!"
Nailed.
For several years at least the main practical hurdles to success as a data science have been about industry expertise and ability to communicate/champion your work in a corporate environment. The actual data work is easier to pick up than it’s ever been.
I still remember being introduced to Mathematica as a freshman, > 25 years ago, solving my 3 hour high school calculus exam in 30 minutes. It was liberating.
Well said, students standing on the shoulder of giants. That is what education is supposed to be like and that is how knowledge continues to grow to benefit mankind.
Well said! Prof Robert Pigford's PhD thesis was only 45 pages thin. His "simple" equation is the keystone for all mass transfer applications involving soluble boundaries - routine occurrence many, many times at home and in industry.
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