Exactly. I care about how a PhD astrophysicist thinks about data just as much I care how a biologist, psychologist, or sociologist researcher thinks about data.
That is, not so much.
At the PhD and research level the data types and questions are so specific that the analyses often are bespoke (in academia, the more bespoke the analysis, the more you can sell its novelty). PhD-level data analyses have very little relevance outside of its own field.
There is nothing profound to be had here, even if they have the prestigious title of "PhD astrophysicist".
I think that depends on the phd. In biology at least, a lot of the data types and questions seem specific and bespoke, but they are really not. Its often just tabular data you are working with, and you are generating the same models as any other data scientist generates to find significance in tabular data. The only difference is in interpreting the significance of the model output, but the tooling is often the same as in a lot of fields. This is why PhD computational biologists have no problems pivoting into all sorts of distally related industries from pure biology, to data science, to computer science, ad tech, or even management consulting.
It's certainly interesting when we stop to think about the elevated value we place on so many different job titles, viewing people as inherently super intelligent and failing to allow room for them to be humans as fallible as the next.
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Exactly. I care about how a PhD astrophysicist thinks about data just as much I care how a biologist, psychologist, or sociologist researcher thinks about data.
That is, not so much.
At the PhD and research level the data types and questions are so specific that the analyses often are bespoke (in academia, the more bespoke the analysis, the more you can sell its novelty). PhD-level data analyses have very little relevance outside of its own field.
There is nothing profound to be had here, even if they have the prestigious title of "PhD astrophysicist".
I think that depends on the phd. In biology at least, a lot of the data types and questions seem specific and bespoke, but they are really not. Its often just tabular data you are working with, and you are generating the same models as any other data scientist generates to find significance in tabular data. The only difference is in interpreting the significance of the model output, but the tooling is often the same as in a lot of fields. This is why PhD computational biologists have no problems pivoting into all sorts of distally related industries from pure biology, to data science, to computer science, ad tech, or even management consulting.
It's certainly interesting when we stop to think about the elevated value we place on so many different job titles, viewing people as inherently super intelligent and failing to allow room for them to be humans as fallible as the next.