It most likely depends on where you want to go. Python definitely has the higher usefulness for data science and machine learning in the narrower sense. But if you want to go deeper into high performance computing or work really close to the periphery, then you will benefit much more from C++. I learned C++ first and later Python in my studies. Looking at some of my colleagues, that doesn't seem to have been the worst way to go. Of course, others are also right when they advise you not to put too much weight on the choice of a programming language. It's just that Data Science is very diverse in its manifestations these days. And in some jobs it is very much in demand that you are a passable programmer and not just able to plug Excel macros together. So it does have a certain relevance which tools you can handle.

In many interesting cases this is actually very possible, e.g. broadcasting x.reshape(1, -1) * y.reshape(-1, 1) Rarely do I need to query the shape explicitly. Mostly a squeeze/unsqueeze leads to the goal. And often one knows the structure of the data explicitly, e.g. the dimensionality of a Euclidean space. I don't want to deny the use case for .shape entirely, but I think often it can be avoided.

*reshape