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As you said yourself, you have no performances problem with R and Python, so I do not think you need to learn Julia. However, in fields dealing with larger dat
by ethelward 8y ago
As you said yourself, you have no performances problem with R and Python, so I do not think you need to learn Julia.
However, in fields dealing with larger datasets (e.g. genetics, astrophysics, ...), Python and R are real bottlenecks in data processing pipelines, and are thus often replaced by programs in C/C++/Java/etc. Having a language with the expressiveness and dynamism of Julia on the one hand, and performances in the same order of magnitude than C++ in the other hand is a huge plus.
- johnisgood 8y agoDo they use Fortran in those fields?
- ethelward 8y agoIn astrophysics yes (although I only follow the fields through friends), quite a lot of linear algebra/physics reference libraries are in Fortran, and new programs are still being developed in the more modern versions of it. In Bioinformatics, not at all. Or at least in no direct way that I'm aware of. Sure, R packages indirectly call upon Fortran libraries (LAPACK et al.), but user facing programs are mostly, from my experience, written in C, C++, Java, Python & Perl.