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The running assumption on HN seems to be that ML/data science jobs are easy because you can just "plug in" already implemented algorithms and just expect things
by Puer 8y ago
The running assumption on HN seems to be that ML/data science jobs are easy because you can just "plug in" already implemented algorithms and just expect things to work. I think the barrier for most of these jobs is a proper statistics education--one that involves active exploration of real world data that's imperfect and has to be cleaned. Very rarely is there an obvious solution to big data problems and much of the work is tedious parameter tuning that absolutely does require knowledge of the math and domain and not just how to write k-Means.
- gaius 8y agoI think the barrier for most of these jobs is a proper statistics education--one that involves active exploration of real world data that's imperfect and has to be cleaned. It's a bit of both really. If the data science job is in the field of predictive maintenance for example, then a (relatively) simple model may be sufficient to add business value straight away, and the hard part is a deep understanding of the potential failure modes of the machinery you're predicting on and the kinds of sensors used to gather the raw data. There's no "one size fits all". This is one of my favourite papers on the subject, written by one of the lecturers on the DS course I did: https://users.cs.duke.edu/~cynthia/docs/RudinETAL2010.pdf https://users.cs.duke.edu/~cynthia/docs/RudinETAL2010.pdf