I am not the OP but it is not necessarily a contradiction. The underlying goal in Pythonic data-science is that one pushes as much of the computation into pre-

I am not the OP but it is not necessarily a contradiction. The underlying goal in Pythonic data-science is that one pushes as much of the computation into pre-packaged loops that have already been written in a close to the metal language (C, C++, Fortran). Well, that's as far as the intent goes, practice deviates from it by degrees. This does not work quite as well in Java (or JVM) because JNI is just supremely god-awful. JVM semantics are overly strict, this over-specification kills optimization opportunities. Is heavy on memory, I would rather use the memory for loading more data than fill it with overhead. Finally there is this OOP culture that gets in the way. That said, JVM is one of the most mature, and well engineered VMs out there (Java is another story), but not very well suited for number crunching, because there is more to number crunching than calling BLAS APIs.