I'd add math skills, but I really appreciate this comment. Hyping the difficulty and complexity of these tasks to protect economic turf - and insisting on accreditation and experience vs. actual results - is never a good long-run play.

The only value any professional can reliably claim to add - data scientist, software architect, CEO, etc. - is the value they can prove. One must be able to justify their job or salary by superior results (quantitatively better predictions, lower costs, faster development cycles). If you can't do this, but instead insist your superior training protects the company from some scary, unseen, vague 'bad consequences' of not having a 'true professional' performing your work, then you're just as big a drain on enterprise value as a lawyer.

Or worse, an MBA. :)

You missed the stats and the math.

As practicing data scientists have pointed out in this thread, that is the least part of the job. And, really, how hard is it. As icelander points out, it comes down to plugging data into standard algo's.

'As practicing data scientists have pointed out'

The OP and resulting discussion is about whether people calling themselves (or having titles saying) 'data scientists' actually are 'scientists' or know anything about 'data'.

so one guy having a 'data scientist' title and spending his time plugging data into black boxes says very little about what 'the least part of the job' in general is.

If anything your "some social skills and some database skills" is an even poorer description. By that measure, the business analyst next door who can write some SQL scripts is a 'data scientist'. But then otoh, maybe he is, who knows? ;)

Edit: I just noticed you are the "Are there practical applications for proving theorems? It seems like it's the full employment act for pencil pushers." guy.

http://news.ycombinator.com/item?id=4634969

My apologies for replying to your comments. Won't happen again. (beware @jacquesm !)

> (beware @jacquesm !)

I should log out of here anyway, thanks for the reminder

chmod 444 news.ycombinator.com

edit: I thought you were agreeing with that slandering fool plinkplonk so I got a little out hand. Sorry jacquesm, i've edited it all out.

There isn't a thing in what I wrote here that warrants this comment.

I don't think there's such a huge body of knowledge in "data science" as people claim. It's not astronomy or biology etc. As a "data scientist" the predictive ability of your model is all that counts. So the social skills and basic knowledge of stats + your progrmming skills/creativity is what's important (and the kaggle competitions have borne this out).

edit: Ok, ad hominem attack. And in that thread no one managed to making a convincing case for the utility of millions/billions of people learning maths as traditionally taught. You're probably a data scientist who wants to inflate your salary (there's my attack).

edit: LOL. you're a joke plinkponk, hahaha - being called out on your lies about your chosen profession being hard (so that you can feel special) really gets you doesn't it! http://www.urbandictionary.com/define.php?term=Wiener%20Didd...

Keep it civil man. You can argue your points without calling names. As a party with no horse in this race, I find the conversation interesting enough without the need to get nasty.

A bit of stats and a bit of math are not that hard.

Really knowing your stuff in either one of those fields is hard, knowing them very well and knowing enough computer science to apply it all (properly) is more the work of a small team than a single individual.

People like that are rare. There is nothing stopping anybody from calling themselves 'data scientist'. Just like there is nothing stopping anybody from calling themselves software architect or system administrator.

In the end that's just people marketing themselves as good as they know how but that does not mean there isn't a sliding scale between warm body and excellence. I think that is the distinction the article tries to make.

The evidence that is coming out from kaggle doesn't support the claim of teams with experienced specialized skill. Teams of a single student have beat out entire industry of companies (the essay score competition for example).

Kaggle competitors work on small data sets, so algorithmic problems don't really surface there.

Just because your algorithm can "beat" an industry's worth of work doesn't mean it can be implemented in a practical or efficient way.