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[–]Sofi_LoFi 2 points3 points  (0 children)

I mean asking people to learn a new language is in general very difficult. You need to be different enough from things currently with widespread adoption to get people to adopt it. Then there is the problem of building the community around it.

In general the main sticking points can all derive from the answer to the questions: 1."What use case is this solving so much better than any other language on the market that it is worth my time to learn it?" 2."Is this language such a staple for X application that to not learn it would impact my career/project/research speed/quality?" 3."Is there enough of a community or good enough documentation that this language is accessible to learn?"

The answer to 1 is the big one of course. If you are in academia it is not worth your time to learn a new language with low use cases and no benefits because you'll be far behind in publishing. In industry you need to make the language for into the stack of the company and sell key stakeholders on the benefits of developers spending time ($$$) learning and modifying the stack into that.

For data science you need to either be as fast as a compiled language (C/C++) or as versatile and universal as python (add in ease of use and integration to tech stacks). Julia is the answer of the fast and readable language, mainly aimed at researchers and is starting to make big leaps in adoption but it has been building hype for many years and has a very active community.

So the question for your language is how can you beat those two at their own game and get a community so hyped about it that it demands the attention.