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Data science is like security: it's very easy to come up with something that looks really good but is horribly broken in subtle ways that you won't find until i

by joker3 8y ago

Data science is like security: it's very easy to come up with something that looks really good but is horribly broken in subtle ways that you won't find until it's too late. I think there's a lot of value in having lots of people be literate at it, but part of that literacy is understanding that it's a hard problem and that it does need attention from dedicated specialists.

fnbr 8y ago
I would certainly agree with you, but I don't think someone with a master's degree is necessarily an expert. What's important, IMO, is having practical experience in data science, which may or may not come with a graduate degree.
- joker3 8y ago
  No, it takes a lot more than a master's degree. Even a PhD is only really entry-level. There's no substitute for practical experience and talking to someone very experienced. Shame there aren't a lot more very experienced statisticians and data scientists out there.
  - Nomentatus 8y ago
    Shame there aren't more concert pianists out there... but given the leverage big data can give (a small percentage change in what a big organization does can still save or earn big bucks) this is a competitive position, just as playing concerts is only for the few. You want a better employee than your competitors have. You'll take all the expertise you can get, plus the ability to invent new techniques as needed. Everybody wants the very best 'cause that last 1% of skill can still be worth a lot of money. Everybody wants someone who can hit a lot of long balls for their team, as it were. There might be a floor on the skill level you could make some use of, but there's no ceiling.
mywittyname 8y ago
> it's very easy to come up with something that looks really good but is horribly broken in subtle ways that you won't find until it's too late. My experience has been that this doesn't matter in a lot of cases. A product can be both, highly useful and subtly broken. One of the biggest boons to my productivity was realizing when something is good enough and to move on. You can waste so much time tuning for precision and recall.
claytonjy 8y ago
Along the same line, I find the hardest thing to get good at for junior data-sciency people is how to tell if your model sucks. Too easy to trick yourself into thinking you have a good model. I think this is an essential, foundational skill for modern data science, not unlike some knowledge of R or Python, but an order of magnitude or two harder to learn. A lot of the difficulty is in lack of good educational material. I also think this is significantly more important than a deep understanding of various modeling approaches; you can learn that as you go, so long as you know if what you have now is "good enough" or if you need a fancier approach. Unfortunately you can't just throw k-fold CV around, average some fit metrics, and call it a day.

by joker3 8y ago

fnbr 8y ago

I would certainly agree with you, but I don't think someone with a master's degree is necessarily an expert. What's important, IMO, is having practical experience in data science, which may or may not come with a graduate degree.

joker3 8y ago
No, it takes a lot more than a master's degree. Even a PhD is only really entry-level. There's no substitute for practical experience and talking to someone very experienced. Shame there aren't a lot more very experienced statisticians and data scientists out there.
- Nomentatus 8y ago
  Shame there aren't more concert pianists out there... but given the leverage big data can give (a small percentage change in what a big organization does can still save or earn big bucks) this is a competitive position, just as playing concerts is only for the few. You want a better employee than your competitors have. You'll take all the expertise you can get, plus the ability to invent new techniques as needed. Everybody wants the very best 'cause that last 1% of skill can still be worth a lot of money. Everybody wants someone who can hit a lot of long balls for their team, as it were. There might be a floor on the skill level you could make some use of, but there's no ceiling.

mywittyname 8y ago

> it's very easy to come up with something that looks really good but is horribly broken in subtle ways that you won't find until it's too late. My experience has been that this doesn't matter in a lot of cases. A product can be both, highly useful and subtly broken. One of the biggest boons to my productivity was realizing when something is good enough and to move on. You can waste so much time tuning for precision and recall.

claytonjy 8y ago

Along the same line, I find the hardest thing to get good at for junior data-sciency people is how to tell if your model sucks. Too easy to trick yourself into thinking you have a good model. I think this is an essential, foundational skill for modern data science, not unlike some knowledge of R or Python, but an order of magnitude or two harder to learn. A lot of the difficulty is in lack of good educational material. I also think this is significantly more important than a deep understanding of various modeling approaches; you can learn that as you go, so long as you know if what you have now is "good enough" or if you need a fancier approach. Unfortunately you can't just throw k-fold CV around, average some fit metrics, and call it a day.