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okay, that is in theory.

Here is the syllabus:

https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

Do you think any part of that is unreasonable or putting the cart before the horse?

They did a good job on this syllabus. Something had to give, and it is math and engineering depth. No 300 or 400 level courses, like networking or OS. Skipping some close to the metal stuff is probably fine, but I am surprised not to see at least some distributed computing or systems engineering skills. I'd be concerned that a grad could work well within an ideal data env but would get blocked by not being able to engineer processing pipelines.

I've worked with brilliant statisticians would not be able to engineer processing pipelines.

Absolutely. It just requires an environment where other folks are building, maintaining, and adapting data infrastructure to new needs -- e.g. a large company with a decent data engineering investment.

Also, in those specialized roles, a statistian with an advanced degree is likely to get more leverage. So I think it's smart for undergrads to work on being "unblockable" In a variety of environments so they can build experience - which is the spirit of the standard CS undergrad there.

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