[itronitron]

I'm not familiar with academic Data Science programs but I've worked with statisticians for over fifteen years and they are usually very involved on the data engineering side. If they aren't running the systems then they are working closely with those people to test and confirm inputs and outputs before running analyses.

[Bartweiss]

> they are working closely with those people to test and confirm inputs and outputs before running analyses

In terms of data science training, at least, this is often a missing element. It's easy to create classroom tasks that focus on teaching how to do analyses and neglect practical aspects like validating data and sanity-checking results. People pick it up on the job, of course, but I wouldn't be surprised if statisticians get a better academic grounding from things like reasoning about uncertainty.

(It's not a problem specific to data science, either. I've heard plenty of complaints about new engineers who are so used to made-up problems that they don't balk at ludicrous data or results when they start doing real work.)

[dragonwriter]

> It's not a problem specific to data science, either. I've heard plenty of complaints about new engineers who are so used to made-up problems that they don't balk at ludicrous data or results when they start doing real work.

This is one of the reasons I think we need to better integrate technology (and general data analysis follows the same reasoning) across the curriculum: an increasing share of work (and a more rapidly increasing share of good paying work) is knowledge-based work that involves both data analysis and working with people who are doing automation, on top of that which is primarily automation or data analysis. But we don't trash other knowledge skills in relation to automation and analysis, which leaves people specialized in automation and analysis and people specialized in domain skills talking to each other over a wide gap too often, with a lot falling through the cracks.