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I mentor individuals looking to transition into data science. I can say without a doubt that those who do the best (get the best jobs) are those with domain exp
by dannykwells 8y ago
I mentor individuals looking to transition into data science. I can say without a doubt that those who do the best (get the best jobs) are those with domain expertise and who are able to leverage that expertise in a data science realm.
Of course, domain expertise is hard to come by and takes years to develop. I look forward to data science leaving far behind the notion that "you too can become a data scientist with 3 weeks of Python + scikit-learn".
- bilbo0s 8y ago>"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 agoThere are a whole ton of students coming out of one year masters DS programs, who don’t know that much more.
- softwaredoug 8y agoObligatory xkcd https://xkcd.com/1831/ https://xkcd.com/1831/
- softwaredoug 8y agoI 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 agoIn 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 agoComputing 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 agowell, as long as your serious practitioners aren't trying to get hired they should be just fine
- 0815test 8y agoStatistics expertise is also very important. Stats may be less sexy today than "data science", but they were doing "model explainability" before it was even a thing!
- nonbel 8y ago>"but they were doing "model explainability" before it was even a thing!" Do you have an example?
- roman030 8y agoLinear Regression
- nonbel 8y agoCheck this: https://news.ycombinator.com/item?id=19353204 https://news.ycombinator.com/item?id=19353204
- mturmon 8y agoTo an extent, CART. But more classically, any structured statistical model with (eg) terms for variable interactions, measurement noise, and hierarchy.
- tel 8y agoPrior to model explainability was experimental design.
- bradleyjg 8y agoI don't understand what it can possibly be without statistics expertise. If you take away that, what's left? a A not-very-good programmer? I always took data scientist to be a rebranding of statistician. (To be clear there's nothing wrong with rebranding.)
- bloomer 8y agoWell the common description of a data scientist as "better at stats than a programmer and better at programming than a statistician" can also be reframed as "worse at stats than a statistician and worse at programming than a programmer" in most cases. I think it's typically a rebranding of PhD in a science field realized that market for that educational track is very small so rebranded as "hottest job of the next millennium"
- deleted 8y ago[deleted]
- Novashi 8y agoHonestly what should your salary be if you have domain expertise and are good with data science tools (and tools surrounding them like git, CI/CD, etc.)?