I don't understand “data scientist”.
What “scientist” is not a “data” scientist?
If the focus is on extracting patterns and insight from large datasets, without expert knowledge about the particular domains, to assist decision-making, a might be what you need.
I'm happy to be educated by any reading this, though.

@tripu I was told by several non Brits at an IT recruitment fair in London in December that "data scientist" was a job very high on the "needed in the EU" list when they were looking for what course to take at Uni, what what I can gather in the IT world this is a "big data analyst" or someone who can search well on mongodb 😀. getting a degree as one improves chances of a visa.

I'd agree it is a very non descript job title

@tripu it's a very bad umbrella name for a series of very serious jobs. I prefer to split into different roles, but we don't have names for all of them yet.

Basically, either you are a data engineer (ETL, deploy, monitoring) or a machine learning engineer (one shot ETL, modeling, analysis), but there's a continuum between the two roles and one can very well be a mix between the two

In large companies one might even be dedicated to visualization and dashboard and would still be a data scientist

@tripu so a data scientist might very well be a statistician or a physicist for instance, but ideally a data scientist would be better at coding than the previous two.

I stress ideally, because I've seen many and most of them just code, in the sense that they are users of a language and don't really know how to build software

If I understand correctly, data science includes, but is not limited to statistics
It also includes things like machine learning and other stuff

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 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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