[racket] Blog post about Racket

Stephen Chang writes:

 > > That said, it's interesting to look at why Python became such a
 > > popular language in science.
 > 
 > Coincidentally, I just read this article:
 > http://www.talyarkoni.org/blog/2013/11/18/the-homogenization-of-scientific-computing-or-why-python-is-steadily-eating-other-languages-lunch/
 > 
 > Not sure if this is your experience, but from the article and
 > comments, the summary seems to be that python is not great for any
 > one task but "wins" because it enables you to do a bunch of tasks
 > in the same language.

That's an important aspect today, but one that appeared only after
Python became popular in many domains of computational science.  Today
there is a Python library for just about every domain of computational
science, so yes, Python lets you do nearly everything.

 > Interestingly, it seems that Racket can do everything in the
 > article's list as well.

I have doubts about some points on that list:

  - neuroimaging data analysis
  - statistical analysis
  - machine learning

If there are Racket libraries for any of those, I'd like to hear about
them.  There is some statistical stuff in the math library, but a
modern data scientist needs a lot more than that. Plus libraries to
read and write common data formats, which are missing from that
article's list, probably because the author took them for granted.


I see Racket's strength for scientific computing in a very different
aspect: the possibility to define languages tailor-made for expressing
computational models in some application domain. Scientists generally
don't want to "write programs", and when they do, the results are
often not pretty. I'd like to have scientists do science and
programmers write programs. Racket could become the meeting point for
the two professions.

Konrad.