This is so very, very true. The major change appears to be one of scale, rather than any qualitative change. Funnily enough, since I put predictive analytics (what does that term even mean, anyway?) on my CV I've gotten much more attention from recruiters and employers. I guess it sounds so much sexier than statistics.

More seriously though, the requirements to be able to hack up a prototype and talk to people are probably what hold back a lot of people who otherwise have the skills to be good "data scientists", or just scientists.

My current employers told me at interview that they had no data, and in the three months I've been there I've been slowly discovering that they have loads of it, unfortunately in multiple incompatible forms and jealously guarded by different departments. It is rather funny, though a little sad that they were essentially drowning in data and didn't realise it.

I agree that being able to hack up a prototype could really make someone stand out as a data scientist. The Insight Data Fellows program mentioned in the article has a 6 week program where the focus is on learning enough software development to hack a prototype by the end of the program. That could be a good way to go.

gaius, I've been appreciating your comments for a long, long time now. But you are wrong here.

There is a difference. It isn't a difference in fundamentals so much as it is a difference in focus.

Business Analysts give reports to CEOs about customer segments or the projected amounts of signups. They arn't even close to DS or statisticians.

Statisticians tell you about how a drug reacted with a control group or how likely it is that a population feels a certain way given the results of a survey or trial.

Data scientists harness data. They impact every user on a site. "Watch this video" "Follow this user" (recommendations) or "Silently ignore this user's impact on the algorithms that manage where this piece of content should go" (graph analysis) or "What exactly is in this photo" (object recognition) or "What combination of widgets leads to the maximal amount of engagement" (optimization) or "I have this paper that I really like, show me more that are just like it" (recommendations, document classifications, NLP).

It is different. The focus is on users and what they will do or should do or should see. To call them statisticians leads to much less understanding of the value that DS bring. Put me in a room with an actuary from an insurance company. Neither of us could possibly do each others jobs. Neither of us have the others skill set.

Now, both of us could learn and get up to speed on how the other works, but a sys admin and a web developer could swap roles more easily than an actuary and a DS. Yet nobody is complaining that we call devs and sys admins different titles.

That's not a counterargument to his point. You're parsing job titles down to the atom, and concluding that "data scientist" is different than "scientist" is different than "statistician", is different than "analyst". Gaius is saying that this job responsibility has been around for a long time, but that people are reaching to find reasons to give it a new name -- exactly what you're doing.

If you ask me, the phrase "data scientist" is recruiter-speak. I have all of the skills required of a "data scientist". I've done the job of a "data scientist". And other than object recognition, I've developed all of the different product features you mention in your comment. You know how I got the skills necessary to do those things? I was trained as a scientist, and there's no such thing as a scientist without data. A person properly trained to analyze data should be able to effectively and fluidly transfer those skills between domains -- otherwise, they're not actually good at it. There's nothing special about internet products that precludes competent people from doing effective data mining on their logs.

I suspect that the real problem here is that "data science" is Internet Hipster for: "someone who has already worked at an internet company, and knows some statistics". Because when it comes right down to it, your average statistician, chemist or physicist is more skilled at data analysis than 99.9% of the "data scientist" types you meet, but they don't easily press the comfort button for hiring managers at consumer internet companies. Why hire the "risky" ex-scientist, when you can hire the guy who claims to be a designer, a software engineer and a statistician?

Now that you mention it, "data scientist" is almost a tautology.

Tell that to the string theorists.

I agree that data scientist does smell a lot like recruiter/marketing speak. But on the other hand, just because it's a new title doesn't mean it isn't valid. Reducing everyone down to "Scientist" is no more helpful than saying a physicist isn't really a separate job, but just a specialised branch of mathematics. Or for that matter, CS is just a narrow branch of mathematics.

Eventually you have to distinguish new fields from the old, even if they have a lot of commonalities.

Well, yeah...when there's specialized knowledge required for the job (like, say, "physics"), it's obviously a good idea to change the job title.

The problem here is that "data scientist" adds no semantic value above and beyond "scientist". A scientist of data, you say? However will we find such exotic creatures!?

You have a comically narrow definition of statistics, cf. "Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data." (http://en.wikipedia.org/wiki/Statistics)

Right, but you are taking a very web centric view there. What would you call the guy who's work impacted every shopper in a supermarket? There were people doing what is now called "data science" with supermarket loyalty cards, credit cards, frequent flyer programmes, etc looooong before there were "data scientists".