For me as long as a core group appears to be active I’m fine with a communities survivability. Julia’s data-science and plotting have continued to improve in te

For me as long as a core group appears to be active I’m fine with a communities survivability. Julia’s data-science and plotting have continued to improve in terms of documentation and feature parity, both are critical in an immature ecosystem as they indicate an active core group of developers. Also many libraries appear to be driven by academics creating cutting edge libraries or developing "workhorse" libraries. One good example is Steven G Johnson’s involvement in Julia [1,2], since he created the FFTW library and NLOpt I’d put him in the category of ‘prolific data science contributor’. Or are take the Julia GaussianProcesses.jl [3] library which has a surprisingly thorough implementation along with academic research (and its citable!) for speeding up GP fitting. Pretty cool! Plus it’s pretty performant to say use Otim.jl to optimize the "hyper parameters" for a set of GP’s. That enables a lot more iterations of data exploration. Essentially the base ecosystem of a language is driven by a core group of contributors and the derivation an ability of that group matters more than most other factors. When doing scientific and or data science I personally care more about the core quality and what the platform enables. Lately I’ve considered learning R as it has a lot of well done says which simply aren’t available in Python, and aren’t ready yet in Julia. Last time I tried to calculate a confidence interval in Python for an obscure probability function I ended up wanting to pull out my air in frustration. There’s libraries that kind of handle it in Python but they are (we’re?) nigh impossible to modify or re-use for a generalized case. Much less getting a proper covariance matrix with enough documentation to know what to do with it. I used R examples to figure out the correct maths. R’s NSE seems appealing in allowing generalized re-use. I’ve had similar ability to re-use library features in Julia for solving problems outside that libraries initial scope. 1: https://en.wikipedia.org/wiki/Steven_G._Johnson https://en.wikipedia.org/wiki/Steven_G._Johnson 2: https://discourse.julialang.org/t/steven-johnson-as-a-juliacon-2019-keynote-speaker/22513 https://discourse.julialang.org/t/steven-johnson-as-a-juliac... 3: https://github.com/STOR-i/GaussianProcesses.jl https://github.com/STOR-i/GaussianProcesses.jl