However, spectrum analysis is not something that data scientists learn at school, yet every mechanical/electronics engineer working in the field knows about it. So, without an expertise in a particular field, data scientists often reach for a big hammer, when more specialized tools exist and are known to the experts in the field.
it sounds like your point is: "some ways of solving problems are superior to others." i've heard this take a million times. one perspective i'll offer to you is, math is not the only way to solve problems. it's not even the best way in many cases. not everything can be solved by defining a narrow goal, and then having a dispute about the methods, and then picking some objective method and then applying it very optimally, or whatever. this is also on you, as an educator, to understand! i could give a bajillion specific examples.
but first, you have to concede: an analogy nobody understands is bad, and you have to own that, and two, it's not really clear, what exactly is your dispute with Design Thinking? it doesn't have anything to do with user interfaces... so why the hell are you talking about it? why "Design Thinking people"? What is your beef here?
Attempts to undermine their role and turn developers into simple cogs in the machine rub me the wrong way.
I perceive (you might disagree) that Design Thinking, Agile, Scrum, and similar things as attempt for designers, PMs, etc. to insert themselves into the process, not as equal partners, but as people with elevated privileges over software developers.
I don't necessarily disagree with the idea and ideals of Design Thinking. I disagree with the practitioners and their perception of themselves as something special over software developer.
I also think that my original analogy at the top is perfectly understood by a lot of people here as much as I understand the type of people on HN.
Please be careful about generalizing.
I agree that many 'data science' programs don't teach these skills, and you certainly have evidence behind your assertation.
Simply that some data scientists, formally trained or titled by themselves or others, have been known to apply their skills to data without having special knowledge regarding the data.
It is a bit of a cliche in some of our experiences. The consulting company that analyzes data for a decision paralyzed organization, that seeks outside guidance in lieu of getting better leadership, is something I see.
That is a real phenomenon, and despite good intentions, can have all the effectiveness of reading tea leaves.
Because there is always data to be scienced. Competently or not.
But, you are making my point for me here. Most data scientists don't get masters in signal processing. You are also acknowledging that gaining expertise in a particular field was worth pursuing.
I suspect that they should be consulted by data science folks as domain experts.
That said won’t AI replace both? ;)
Since the 2010's data science has gone from scientific based curiosity in solving problems to pure technicians work. There's a set of algorithms they follow, no exceptions allowed. Kaggle is a horrible anti-pattern.
NB: I am a data scientist.
