Cedric St-Jean, thank you for the "Python Paradox" link. It was a very good read, and linked to another article "Revenge of the Nerds" http://www.paulgraham.com/icad.html which makes me think that Julia is a kind of "Lisp meets Fortran".
On Tuesday, December 1, 2015 at 9:43:51 PM UTC, Cedric St-Jean wrote: > > Hi, > > I spend half of my time on data science consulting in Python, and the > other half on research in AI with Julia. Most of my data science work > involves making clever use of already-built methods (i.e. scikit-learn), > and Python's extensive libraries work great for this purpose. Furthermore, > a lot of machine learning can be expressed with linear algebra, and numpy > is very decent for writing my own algorithms. For these reasons and for > Julia's pre-1.0 status, I don't see myself recommending Julia over Python > to a client in at least a year. > > That said, as a data scientist, you shouldn't frame yourself as a "Python > programmer" or a "Julia programmer". You should make sure that you're good > at programming, but your #1 skill will be all the > statistics/probabilities/machine learning knowledge and experience that you > have. Keep reading, keep practicing. If you really want to use Julia, now > is a great time to contribute some random forest implementation to JuliaML. > That will look great on your resume. > > Finally, have you read about the Python Paradox > <http://www.paulgraham.com/pypar.html>? That was written 10 years ago. If > it was written today, it would be called The Julia Paradox. There might not > be a lot of places where you can use Julia, but good data science employers > will look favorably on your Julia experience, even if they ask you to use > Python/R/Matlab in your job. > > Good luck, > > Cédric > > On Tuesday, December 1, 2015 at 3:19:13 PM UTC-5, [email protected] wrote: >> >> Hi Everyone, >> >> >> I'm currently learning data science and I have a cs101 with python >> background. >> >> >> I have this nagging feeling that Julia is going to be huge (and its >> pleasing to code in) and so as I begin to learn stats with python, I keep >> drifting over to Julia. Learning python seems like I'm investing in a stock >> at its peak and its only downhill from there. >> >> >> However, I also have a nagging feeling that its not ready for productive >> data to data analysis or data engineering type production, that the job >> prospects will be slim for a while and that I will spend too much time >> chasing pycall etc errors. >> >> >> Also with python I get can get a backup sysadmin, backend web etc job if >> it turns out I'm terrible at stats. >> >> >> I'm thus vacillating and not sure which language to learn here on out. >> >> >> Are my considerations sound? Any other thoughts on this please? >> >> >> Thanks! >> >
