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There's a huge difference. If I ask "why is this model applicable to this situation?" or even "is this model applicable to this situation?" then I want the prac

There's a huge difference. If I ask "why is this model applicable to this situation?" or even "is this model applicable to this situation?" then I want the practitioner to have the ability to provide and understand the answer. Yes, data science as an industry, but that's because wrong answers are largely undetectable. If I roll out the treatment cell of an experiment but shouldn't have, because I didn't account for within-client correlation, the world marches on and the business thinks they are making data driven decisions. They'll never know they left millions on the table. Or maybe I'm right for the wrong reasons and I was merely more confident than I should have been and the world still marches on oblivious because there are very very few correction mechanisms.

chibg10 7y ago
The academic math types with no ML experience don't fare much better than the no-math practitioners with experience from what I've seen. They generally have terrible mental models of how the math behaves when applied to real data. IME the competent data scientist needs to have a high-level understanding of the concepts underlying ML (stats, optimization, central limit theorem) and to be comfortable reading math to understand the low-level details when they are relevant. In addition to actually understanding the ML techniques they're using obviously. On top of that, I would rather work with someone who can code well than a pure mathematician with a ton of experience proving theorems (and no coding ability). Outside of a few industry research jobs, the cost:benefit ratio of making substantial theoretical breakthroughs in most everyday ML work doesn't justify a lot of time doing theoretical proofs.
- otabdeveloper4 7y ago
  The person you're replying to isn't talking about "theoretical breakthroughs", he's talking about a basic ability to interpret and detect errors in your model. These are abilities that "data scientists" do, indeed, lack.
ivanhoe 7y ago
My impression of working closely with data-scientists is that what you describe comes more from experience, than from purely theoretical knowledge of the math behind. Ideally it's both, but if having to choose, I'd rather bet on someone who did it already a dozen times and knows what to expect.

chibg10 7y ago

The academic math types with no ML experience don't fare much better than the no-math practitioners with experience from what I've seen. They generally have terrible mental models of how the math behaves when applied to real data. IME the competent data scientist needs to have a high-level understanding of the concepts underlying ML (stats, optimization, central limit theorem) and to be comfortable reading math to understand the low-level details when they are relevant. In addition to actually understanding the ML techniques they're using obviously. On top of that, I would rather work with someone who can code well than a pure mathematician with a ton of experience proving theorems (and no coding ability). Outside of a few industry research jobs, the cost:benefit ratio of making substantial theoretical breakthroughs in most everyday ML work doesn't justify a lot of time doing theoretical proofs.

otabdeveloper4 7y ago
The person you're replying to isn't talking about "theoretical breakthroughs", he's talking about a basic ability to interpret and detect errors in your model. These are abilities that "data scientists" do, indeed, lack.

ivanhoe 7y ago

My impression of working closely with data-scientists is that what you describe comes more from experience, than from purely theoretical knowledge of the math behind. Ideally it's both, but if having to choose, I'd rather bet on someone who did it already a dozen times and knows what to expect.