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I think you make some good points, but I think I need to agree with the previous poster, it seems mostly like marketing jargon. I come to this conclusion becau

I think you make some good points, but I think I need to agree with the previous poster, it seems mostly like marketing jargon. I come to this conclusion because these people, who market themselves as "data-scientists", really seem just like statisticians, or am I wrong? Don't all scientists, to some degree work with data? I recently worked at a company who employed "data scientists", they rarely worked in isolation as far as I could tell, and usually required heavy assistance form system/database/storage administrators to be successful and aided by (but not always) with software engineers. In the case of ML, I still see it as just software engineering / computer science. Sure there are pioneers in the field of deep learning, but aren't most of us just leveraging APIs or existing algorithms? Honestly, I'm pretty confused myself lately and I work with some of this tech. Open to enlightenment on this topic by the way.

dhsjsjjsj 10y ago
You're 100% right that "data scientist" is a fancy made-up term for people who mostly do statistics. In the same way, "software engineer" is a fancy made-up term for people who mostly do programming. The fact that data science is basically statistics supports the argument of the guy who is saying data science and software engineering are different jobs. If you are a software engineer and want to be a data scientist, you are free to switch careers. Or maybe work as an engineer for a startup that could use someone to do statistics on the side. But that might be a bad move with no experts around. With data science, your work product is often conclusions and advice. Those are highly susceptible to bullshit. You don't get a compiler or unit tests that tell you that you are coming to the wrong conclusion and giving bad advice. It takes experience and judgement to get that right, not just hacking on an IPython notebook and accepting what the model spits out.
sidlls 10y ago
That wasn't quite my point. I do agree that "data science" is just marketing jargon for "statistics" (and definitely disagree that it's the same as labeling "programmer" as "software engineer", the way another person commented in response to you), but I don't think any and every programmer can "do data science". What you describe with respect to data scientists needing help is what I'd call "data engineering." A data science person who is supposed to be doing production-facing engineering should be a competent enough data engineer that he or she can extract information from the data stores without having to have outside help most of the time, but strictly speaking, in my view, these are two different kinds of development.
- derefr 10y ago
  The "data science" vs "data engineering" distinction helps a lot. Let me try to re-make my original point in light of it. There are a few Bigcorps with real, novel Big Data datasets that need original statistical work slung at them. These companies need data scientists, for real: people with Ph.Ds in statistics. These people are indeed not interchangeable with programmers. They're partially interchangeable with academic computer scientists, and mathematicians, and physicists; but not with any trades-workers, including your average programmer. Their job is Science, capital-S: to do sound, replicable, high-powered experiments to learn things about the data. That is a skill in-and-of-itself, and one you need a lot of practice with—under the scrutiny of people like Journal editors—to get right (because it's hard to tell from your own observation if you've got it wrong.) Everyone else, though—that is, all the companies but the SV Big Five and maybe some mobile-game and advertising firms—when they say they're looking for "data scientists", all they're really looking for is data engineers. The actual "data science" they're trying to do is a solved problem: you can find out the same thing a data scientist would tell you from five minutes of Googling the problem, because dozens of companies have solved the same problem for their own data-sets already and written case-studies on how they did it. You only need to implement said solution. Which, like I said, any competent programmer could do, after a few hours spent reading the API reference of a statistics package. The majority of "data science" (read: data engineering) jobs aren't looking for people to Do Science; they're looking for regular programmers clever enough to have self-selected into the higher caste by doing nothing more than having learned some statistics-package APIs and then marketing that skill. (Much like the "officer class" of the American military in the 1800s allowed people to self-select into it just by being voracious readers.) It's basically an implicit open question at the beginning of the hiring process, "what do you want to be doing all day?", that is answered by choosing a different (pretend) job title.

dhsjsjjsj 10y ago

You're 100% right that "data scientist" is a fancy made-up term for people who mostly do statistics. In the same way, "software engineer" is a fancy made-up term for people who mostly do programming. The fact that data science is basically statistics supports the argument of the guy who is saying data science and software engineering are different jobs. If you are a software engineer and want to be a data scientist, you are free to switch careers. Or maybe work as an engineer for a startup that could use someone to do statistics on the side. But that might be a bad move with no experts around. With data science, your work product is often conclusions and advice. Those are highly susceptible to bullshit. You don't get a compiler or unit tests that tell you that you are coming to the wrong conclusion and giving bad advice. It takes experience and judgement to get that right, not just hacking on an IPython notebook and accepting what the model spits out.

sidlls 10y ago

That wasn't quite my point. I do agree that "data science" is just marketing jargon for "statistics" (and definitely disagree that it's the same as labeling "programmer" as "software engineer", the way another person commented in response to you), but I don't think any and every programmer can "do data science". What you describe with respect to data scientists needing help is what I'd call "data engineering." A data science person who is supposed to be doing production-facing engineering should be a competent enough data engineer that he or she can extract information from the data stores without having to have outside help most of the time, but strictly speaking, in my view, these are two different kinds of development.

derefr 10y ago
The "data science" vs "data engineering" distinction helps a lot. Let me try to re-make my original point in light of it. There are a few Bigcorps with real, novel Big Data datasets that need original statistical work slung at them. These companies need data scientists, for real: people with Ph.Ds in statistics. These people are indeed not interchangeable with programmers. They're partially interchangeable with academic computer scientists, and mathematicians, and physicists; but not with any trades-workers, including your average programmer. Their job is Science, capital-S: to do sound, replicable, high-powered experiments to learn things about the data. That is a skill in-and-of-itself, and one you need a lot of practice with—under the scrutiny of people like Journal editors—to get right (because it's hard to tell from your own observation if you've got it wrong.) Everyone else, though—that is, all the companies but the SV Big Five and maybe some mobile-game and advertising firms—when they say they're looking for "data scientists", all they're really looking for is data engineers. The actual "data science" they're trying to do is a solved problem: you can find out the same thing a data scientist would tell you from five minutes of Googling the problem, because dozens of companies have solved the same problem for their own data-sets already and written case-studies on how they did it. You only need to implement said solution. Which, like I said, any competent programmer could do, after a few hours spent reading the API reference of a statistics package. The majority of "data science" (read: data engineering) jobs aren't looking for people to Do Science; they're looking for regular programmers clever enough to have self-selected into the higher caste by doing nothing more than having learned some statistics-package APIs and then marketing that skill. (Much like the "officer class" of the American military in the 1800s allowed people to self-select into it just by being voracious readers.) It's basically an implicit open question at the beginning of the hiring process, "what do you want to be doing all day?", that is answered by choosing a different (pretend) job title.