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If you learn web development and apply for a job as computer scientist, there's a bit of a mismatch. I think the author is assuming that "data scientist" means

If you learn web development and apply for a job as computer scientist, there's a bit of a mismatch. I think the author is assuming that "data scientist" means scientist more than developer. But I agree, there's more than enough applications coming out of this for developers.

maciejgryka 11y ago
Now it gets much more fuzzy, but for the sake of the argument (after all why are we here? ;)) I'm willing to bet that there are lots of data science jobs that could be done well given a good knowledge of how to use deep learning and no understanding of how EM works. I'm not saying that knowing fundamental (and/or difficult) topics is not useful - it absolutely is! It's just a matter of prioritizing what you learn about. I think if you want to maximize your impact, it makes sense to invest in learning the currently-most-promising tool before going for things with lower reward/effort ratio.
- vivekn 11y ago
  The problem with doing that is you keep reinventing the wheel. Use deep learning if that fits the problem, but keep learning about other things like how EM works, variational inference, graphical models etc on the side. One day you might find a problem where deep learning doesn't work as well as some of the other techniques. Sure there are data science jobs that can be done without much knowledge, but people tend to stop when they see math and are just happy to use some API. This IMO is a wrong approach.
  - maciejgryka 11y ago
    You're completely right. There's a cool quote from http://waitbutwhy.com/2015/06/how-tesla-will-change-your-life.html http://waitbutwhy.com/2015/06/how-tesla-will-change-your-lif...: "I’ve heard people compare knowledge of a topic to a tree. If you don’t fully get it, it’s like a tree in your head with no trunk—and without a trunk, when you learn something new about the topic—a new branch or leaf of the tree—there’s nothing for it to hang onto, so it just falls away. By clearing out fog all the way to the bottom, I build a tree trunk in my head, and from then on, all new information can hold on, which makes that topic forever more interesting and productive to learn about. And what I usually find is that so many of the topics I’ve pegged as “boring” in my head are actually just foggy to me—like watching episode 17 of a great show, which would be boring if you didn’t have the tree trunk of the back story and characters in place." I think learning to use APIs is probably the way to start, but it definitely pays off to keep digging deeper as you go along.

maciejgryka 11y ago

Now it gets much more fuzzy, but for the sake of the argument (after all why are we here? ;)) I'm willing to bet that there are lots of data science jobs that could be done well given a good knowledge of how to use deep learning and no understanding of how EM works. I'm not saying that knowing fundamental (and/or difficult) topics is not useful - it absolutely is! It's just a matter of prioritizing what you learn about. I think if you want to maximize your impact, it makes sense to invest in learning the currently-most-promising tool before going for things with lower reward/effort ratio.

vivekn 11y ago
The problem with doing that is you keep reinventing the wheel. Use deep learning if that fits the problem, but keep learning about other things like how EM works, variational inference, graphical models etc on the side. One day you might find a problem where deep learning doesn't work as well as some of the other techniques. Sure there are data science jobs that can be done without much knowledge, but people tend to stop when they see math and are just happy to use some API. This IMO is a wrong approach.
- maciejgryka 11y ago
  You're completely right. There's a cool quote from http://waitbutwhy.com/2015/06/how-tesla-will-change-your-life.html http://waitbutwhy.com/2015/06/how-tesla-will-change-your-lif...: "I’ve heard people compare knowledge of a topic to a tree. If you don’t fully get it, it’s like a tree in your head with no trunk—and without a trunk, when you learn something new about the topic—a new branch or leaf of the tree—there’s nothing for it to hang onto, so it just falls away. By clearing out fog all the way to the bottom, I build a tree trunk in my head, and from then on, all new information can hold on, which makes that topic forever more interesting and productive to learn about. And what I usually find is that so many of the topics I’ve pegged as “boring” in my head are actually just foggy to me—like watching episode 17 of a great show, which would be boring if you didn’t have the tree trunk of the back story and characters in place." I think learning to use APIs is probably the way to start, but it definitely pays off to keep digging deeper as you go along.