> P.S. It's called ’statistics’.

No, it's not. Read Breiman's "Statistical Modeling: The Two Cultures."

http://www2.math.uu.se/~thulin/mm/breiman.pdf

"Statistics" has largely been concerned with the "data modelling culture" Breiman talks about; a lot of data science is focused on algorithmic modelling, things like neural nets, random forests, and so on. A lot of these techniques have been refined outside of modern statistics because of statistic's focus on data modelling.

This also ignores all of the things that fall outside of the purview of the modelling steps altogether, things like data cleaning, data engineering, and so on. All of those are properly "data science" but often fall outside of what's in a statistics textbook.

If you are going to do data science, you should know statistics. You should know a lot of it. But that is far from the only thing you should know.

As to your larger point... yeah, well, jobs allow people to eat and get health insurance and all that, so it's understandable that OP might want to be able to do those things and not just apply it for his own pleasure. My take on that is that it's hard, both to acquire the skills needed and to signal to employers that you have them. If you're going that route, you need to build a solid portfolio of work. Kaggle might be a good place to start.

I agree with this observation. And given data science's distinguishing emphasis on algorithms and process, maybe it would be more properly called "data engineering."

on topic: https://xkcd.com/1831/

I wasn't assuming that at all. Just wondering about the real world prospects for someone self taught.

> So is it a waste of time to learn it on the side with the hopes of getting a data science job ?

Not at all. Worst case scenario even if it doesn't land you a data science job, doesn't mean that it won't greatly help in another job that you may be doing.

The question you're asking is essentially "what's the point of going to the gym if I won't ever become Mr. Olympia".

I'm not the OP.

The comment above responded to the OP by treating his/her question as trivial and followed it with a snarky "P.S. It's called ’statistics’." remark.

P.S. @qubex Some people don't have degrees and would actually like to work in data science regardless. The OP is asking about the practicality of that scenario.

I didn't treat it as trivial (exactly) but I oozed what I judge a suitable level of condescension for what is clearly a very venal question that transpires a very narrow horizon. As for the ”it's called ’statistics’” comment: it is, and that subject has a rich history going back centuries — much of the ’modern’ ”data science” is just a less refined, more brutal reinvention of those same techniques.

I run a data science team.

We have statisticians (yes, plural) on my team who have published in Nature, and plenty with other backgrounds.

Even ignoring the data engineering side, there is plenty that statisticians don't do or know which is useful data science.

Take the two attitudes to p-tests, or what a "reasonable number of features" means. You drop the Jeff Dean "consider training models with billions of features" quote on a statistcians desk and see their eyes open.

Statistics is great, but data science is just as much programming as it is stats.

Awesome, so what qualifications do your Data Science team members have and would you hire someone without a degree? Or what if they had a data science / ML / AI MOOC like Udacity or Coursera? And do you have any advice for OP or others who don't have degrees?

We've interviewed people without degrees for SWE positions. I don't think we've hired any.

I wouldn't rule it out, but it would be useful to have accomplishments to point to, or a personal recommendation.

We have a stack of maybe 25 well qualified PhD data scientists waiting for interviews. It takes a lot to get to the head of that line.

Interesting. So perhaps tales of shortages are bullshit?

It's pretty hard to find people who know how to apply the tools that they have to new problems.