As I said in another comment, the familiar python like syntax, while having the potential for much better performance.
Many data scientists are familiar with Python. They can write similar code and will run much faster by default (or with very little tweaking).
Then, if that's not fast enough, The language is positioned such that you can optimize the code to a much higher degree than you can with Python.
I'm not saying that Python code can't be optimized, but there is a limit to the speed of python code even with optimization. At some point you have to drop down to a C extension (what Numpy, Pandas, etc... does) and then you are just writing glue code in Python to drive the C. You could also use one of the Python optimized compilers, but that has limits and edge cases as well. There is "developer friction" there.
In Nim you can just have the whole program in one language. The program can be optimized to the same level as optimized C code with no special tools. This is much less friction.
What's missing is the libraries. But that will come with time.
