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Comment on How a PhD astrophysicist thinks about dataparent

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I obtained a PhD in {astrophysics, theoretical physics, quantum physics, computational physics} a few years ago. Choose one of these keywords: Which one is most catchy? I second you, astrophysics just sounds cool in most cases, whereas quantum physics is obviously much of an advantage if you talk to quantum computing (and AI) people.

The bottom line is: In this kind of science, you are used to "big data", to massively parallel computing, to computation and statistics on various levels of abstraction. But the engineering skills outcome greatly vary, because from the physics perspective, computers are just a tool for getting the job done.

For instance, big data in astrophysics is quite different from big data in accounting. Complexity in astrophysics programming is also very different from complexity in the banking buisness. People tend to get arrogant due to their years-long experience, but in the end all they have is just years-long experience in that particular domain, let it be buisness or physics.

Not to mention programming isn't a sport.

I don't care who has the best paint brush technique. What is interesting is the art and how to creatively solve an interesting problem. There is no shortage of people who can masterfully paint absolutely uncreative shit.

This article though didn't really get deep enough to learn much from.

How different is astrophysics from quantum physics? I would think quantum physics has a wide range of applicability over astrophysics when it cones to the variety of fields the research can impact, but this is just a guess based on my limited understanding.

They are the essentially the same and radically different.

That is to say, if you have to ask, be skeptical of any terse answer.

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