I find creative ways to abuse data using the Python data science stack to fight propagandists. I do academic research with DePaul's Divergent Design Lab, and data analytics at Hu-Friedy.
I'm impatient and posted a similar question on Twitter, and got a few different responses, along with some cool use cases:
What’s the project that makes @JuliaLanguage worth it versus #Python? While it’s faster, speed isn’t much of an issue for me, and with the learning curve, and comparative lack of support makes it seem like the speed gains wouldn’t be worth it.
It seems like in addition to speed gains, it's commonly used for probabilistic modelling, with lots of serious work in that area, and multiple libraries in support of that functionality, see here.
There were a couple use cases, for example in land-surface models, where there's a lot of data, requiring speed and the ability to process that much information, they need to be able to build ML models, and use intuitive statistical tools for analysis at the end, so Julia is able to handle each aspect of their work see here.
There is a lot of support for differential equations, see here.
Other Cool Things I Learned:
While Julia doesn't have as many established resources, there are great and friendly communities to support it, see here.
People are able to use it very efficiently and code is very recyclable from one project to another, allowing for faster development. This is something that was brought up multiple different ways by different people, see here.
Anything that's not available in Julia but is in python can still be leveraged using PyCall, see here.
Side-Note:
For more information, JuliaCon is free this year, and you can register here. Previous years are also available (2019, and (2018)
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Update
I'm impatient and posted a similar question on Twitter, and got a few different responses, along with some cool use cases:
Use Cases:
Other Cool Things I Learned:
Side-Note:
For more information, JuliaCon is free this year, and you can register here. Previous years are also available (2019, and (2018)