and you draw the line at whatever is needed for
But that’s quite arbitrary. MATLAB and Octave come with pretty terrible statistics support but that’s fine. However, if they pulled out the ODE solvers people would rebel because that’s such core functionality. So why wouldn’t you say it’s a core functionality?
Because what’s core is personal, except for the extreme basics. Julia is trying to make Base be those extreme basics because otherwise everything is core to some segment of the population and you get bloat.
How do you handle it? You could just tell your class about the 5 or so packages to use. Or if it’s just stats, point them to JuliaStats. It really shouldn’t take more than 5 minutes to introduce the stats packages. If you want it pre-installed, as @aaowens suggests, just have them install JuliaPro which was created exactly for this purpose.
But you’re not going to convince anyone that the top 100 packages should go to the Julia Base repository, or even that the top 3 packages you care about should. Julia’s already been there, and what it does is the opposite of what you’re thinking. Packages in Julia Base cannot update regularly because they are tied to Julia releases. They are harder for contributors to jump into since there’s so much other code around them. They are harder to test because they are tested with the rest of Julia. In the end what it does is cause stagnation due to the inertia of larger repositories, while on its own DataFrames.jl is nimble and can release bugfixes almost instantly. Julia had a lot of stuff in Base and is getting leaner for this reason.
This is getting off-topic now and so it should continue in that other thread.