By my understanding, a data scientist specifically concerns themselves with analysing large datasets. There's a big overlap with various other fields.
In astronomy, for instance, we're collecting so much data that it's difficult to analyse it all. It starts to pile up. And there's such a huge amount that some people (myself included) have spent years analysing data that others have acquired. Discoveries are still being made this way with second hand data.
Data science (including disciplines like data mining and deep learning) works to use this vast amount of data as best they can. It feeds synergistically back into the disciplines the data came from.
Though I'm not a data scientist, and I'd be interested to hear other takes on this.
I know the scale of the datasets has changed, and also the tools used to work with them, but -- isn't that kind of analysis what #statisticians always did? A quantitative leap, big as it be, doesn't justify changing job names...
Journalists work very differently today, and still we call them "journalists", not "information specialists"; musical composition, production and consumption have changed a lot, yet we don't talk of "auditive designers" but of "musicians". etc.
I mentioned data mining and deep learning above. Neither of these are covered by statistics, but both are incorporated into data science.
Statistics is a part of data science in the same way algebra is a part of physics. The two are linked but not synonymous.
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