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>"you too can become a data scientist with 3 weeks of Python + scikit-learn"... I don't think any serious practitioners were out there thinking in this manner.

>"you too can become a data scientist with 3 weeks of Python + scikit-learn"... I don't think any serious practitioners were out there thinking in this manner. But there is a gold rush right now, and any gold rush will attract its share of charlatans.

cschmidt 8y ago
There are a whole ton of students coming out of one year masters DS programs, who don’t know that much more.
softwaredoug 8y ago
Obligatory xkcd https://xkcd.com/1831/ https://xkcd.com/1831/
softwaredoug 8y ago
I see it all the time in my domain (search). Novice data scientists getting into a new field quickly get frustrated with existing practitioners. Existing practitioners want to use tried and true methods; data scientists want to reinvent the field with ML. The existing practitioners don’t know how to manage the usually black-box ML solution. Data scientists don't know the domain enough to be effective. So it doesn’t go far. It’s a dynamic that sadly burns out overly optimistic, but smart data scientists and sadly leaves a negative impression on existing practitioners on the promise of ML. Those practitioners stick their head back in a hole instead of innovating. All to say with more explainable ML (and more humility on everyone’s part) more progress would be made.
minimaxir 8y ago
> I don't think any serious practitioners were out there thinking in this manner. But a lot of thought pieces / YouTubers are pushing it, which is a problem.
XuMiao 8y ago
In fact, many quantatitive researchers in their domain are already data scientists without using modern tools like machine learning. Being able to use these tools is only the beginning of a new practicing and advancing. I consider it the responsibility of computer scientists and engineers to make the tools better and easier. But unfortunately, it's that one has to become a good computer scientist first then a domain expert. We are not doing enough on innovations of tooling.
- Jedi72 8y ago
  Computing people in general seem to have forgotten the whole reason our field exists is to build tools for other people, the ones who do the "real work". With the possible exception of video games nothing we do with computers is useful or interesting on its own, its only point is to assist other fields. So we can have all the advances in the world but if we cant make them into useful tools, what was the point.
itronitron 8y ago
well, as long as your serious practitioners aren't trying to get hired they should be just fine

cschmidt 8y ago

There are a whole ton of students coming out of one year masters DS programs, who don’t know that much more.

softwaredoug 8y ago

Obligatory xkcd https://xkcd.com/1831/ https://xkcd.com/1831/

softwaredoug 8y ago

I see it all the time in my domain (search). Novice data scientists getting into a new field quickly get frustrated with existing practitioners. Existing practitioners want to use tried and true methods; data scientists want to reinvent the field with ML. The existing practitioners don’t know how to manage the usually black-box ML solution. Data scientists don't know the domain enough to be effective. So it doesn’t go far. It’s a dynamic that sadly burns out overly optimistic, but smart data scientists and sadly leaves a negative impression on existing practitioners on the promise of ML. Those practitioners stick their head back in a hole instead of innovating. All to say with more explainable ML (and more humility on everyone’s part) more progress would be made.

minimaxir 8y ago

> I don't think any serious practitioners were out there thinking in this manner. But a lot of thought pieces / YouTubers are pushing it, which is a problem.

XuMiao 8y ago

In fact, many quantatitive researchers in their domain are already data scientists without using modern tools like machine learning. Being able to use these tools is only the beginning of a new practicing and advancing. I consider it the responsibility of computer scientists and engineers to make the tools better and easier. But unfortunately, it's that one has to become a good computer scientist first then a domain expert. We are not doing enough on innovations of tooling.

Jedi72 8y ago
Computing people in general seem to have forgotten the whole reason our field exists is to build tools for other people, the ones who do the "real work". With the possible exception of video games nothing we do with computers is useful or interesting on its own, its only point is to assist other fields. So we can have all the advances in the world but if we cant make them into useful tools, what was the point.

itronitron 8y ago

well, as long as your serious practitioners aren't trying to get hired they should be just fine