[danielscrubs]

I’ve met some really great self-learned programmers. Never had anyone self learned even been able to make a basic inductive proof, but they will still call themselves kings in CS.

Got a bit sick of the attitude that CS is programming. Switched to Data Science and after a while I’m starting to see Data Scientists that can’t do even basic math. With 6 lines of copy pasted code they’ve made a dnn. They know how to separate into test sets and that’s it. I really feel we need certifications that people actually respect because this is just the ultimate lemon market.

Now my colleagues are just PhDs and I couldn’t be happier. But still I do worry about the field. What will math heavy fields do in the future? Slap theoretical in front of the course as to not make self-learners self-conscious?

[bumby]

I suspect every technical field experiences this. For example, a surprising number of mechanical engineers I’ve worked with don’t know the difference between longitudinal stress and hoop stress derivations and the resulting impacts on design, yet they regularly design pressure vessels.

I think the reason has multiple dimensions:

1) most jobs, outside of fundamental R&D don’t require deep levels of understanding because they are more in the vein of “get ‘er done” type of work. Truth is, PhDs are over qualified for many (most?) jobs

2) some people simply want a credential and do a brain dump immediately after university

3) as you alluded to in a different comment, hiring managers often don’t have the technical chops to separate the wheat from the chaff

[non-entity]

Haven't data scientists been watered down to effectively glorified data analysts who use programming languages and libraries as tools?

[danielscrubs]

It’s just a lemon market that seems to get worse with time, everyone says that they can do anything to get a foot in the door.

Heck one of my friends has more than double my salary because he said he was a specialist in a marketing software he never heard of before the interview. Now, a year later no one is the wiser and he can buy a new Tesla twice a year (still jealous).

I think a lot has to do with bosses that never started from the bottom so they aren’t great at interviewing, because they have no clue about non-management things. Then they have no clue how productive people should be or even what to measure besides “Sprint points”.

[orwin]

It really depends on the project. My company is hosting multiple ML/AI projects, some with datascientist that are, as you said, glorified data analysts. Usually MBAs or mixed cursus, but also CS guys (my favourite clients as they will never tell you "i can't ssh onto my server" after executing `chmod -R 777 /etc/`).

And some with genuine DS/statisticians. Also the first kind of project almost always end up hiring statisticians in the end, so realistically, having "glorified data analysts" that can sell to the consortium or kickstart project is enough.