“Full-stack” data scientist means mastery of machine learning, statistics, and analytics. Today’s fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning the darlings of the job market. Alternative challengers for the alpha spot come from statistics, thanks to a century-long reputation for rigor and mathematical superiority. What about analysts?Whereas excellence in statistics is about rigor and excellence in machine learning is about performance, excellence in analytics is all about speed. Analysts are your best bet for coming up with those hypotheses in the first place. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role. Of the three breeds, analysts are the most likely heirs to the decision throne.
The top trophy hire in data science is elusive, and it’s no surprise: a “full-stack” data scientist has mastery of machine learning, statistics, and analytics. When teams can’t get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Which of those skills gets the pedestal?
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https://hbr.org/2018/12/what-great-data-analysts-do-and-why-every-organization-needs-them?fbclid=IwAR2ULJhJK4SzmgzOtsqOFuyheMkTUNThdJmSCgMkSMBDGYoxmEplQs85gfo
