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Perl in data science: could a grant from Perl foundation be useful?

by zubenel0 (Sexton)
on Feb 18, 2020 at 19:15 UTC ( #11113122=perlmeditation: print w/replies, xml ) Need Help??


Recently I was thinking about if it is possible to make Perl a more attractive option for data science. I know that some great initiatives exist like RFC: 101 Perl PDL Exercises for Data Analysis or RFC: 100 PDL Exercises (ported from numpy). On my part, I will try to write a blog post with a particular machine learning task I have chosen. Nevertheless, as Ovid wrote falling short in data science field is a significant drawback of Perl. How to fix this?

What I thought about as a way to to proceed could be a grant from Perl foundation. It could work only if it would be possible to find someone interested in a project related to Perl and data science and capable to do it. IMO one of the solutions that could help would be to write a book on How to use Perl in Data Science. Again, this idea is not mine as it was mentioned in perlblogs as a desire to have a new PDL book. Maybe with a help from Perl foundation such a project could encompass even more than PDL and include several other modules suited for data science.

Another interesting idea that I have encountered was to create Perl/XS graphics backend as there is a need to have graphic library which can create 2D/3D chart easily - see the comments on perlblogs. Unfortunately, I know very little about this but I guess that it might be a very hard task... So these are just a couple of examples but actually the main issue is if it is feasible in general - to have a grant for data science using Perl? What do you think? Do you know someone that could be interested in it? Or do you think that this approach is flawed and have some other suggestions?

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Re: Perl in data science: could a grant from Perl foundation be useful?
by thechartist (Monk) on Feb 19, 2020 at 00:37 UTC

    There is nothing wrong with using Perl for data analysis if you know what you are trying to do. There are a number of options for conducting statistical analyses from a classical POV. Bayesian methods are sadly lacking right now, but you can always call out to R for that.

    If you just want to unleash algorithms on vast quantities of data (of unverifiable quality), Perl has some "machine learning" options, but they are limited, as are tutorials.

    A better use would be to see how machine learning algorithms could improve Perl on systems that do not get a lot of testing. That is what I am focusing on right now. I expect I'll need to write bindings to various C++ libraries, which is not the most appealing of options.

      That seems pretty interesting what are you trying to do, could you elaborate a little more? How do you think machine learning could benefit in improving Perl? As a personal experience, sometimes I encounter troubles by using Perl on Windows and on Linux it works just fine...

        I am trying to formalize a model/process that can ID distributions where a patch on one is highly likely to also work on another. Retrospectively, we can patch multiple models and reduce time bug hunting. Prospectively, we can configure build scripts to eliminate certain problems.

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