The first things I would look for in a data science language are multidimensional arrays, linear algebra packages, data frame and time series libraries ... none of which feature on this page.
Yeah I'm confused. The only "data science" I can see here is the the title.
How is list comprehension a data science primitive? How did this get over 4,000 stars on GitHub with a glaring lack of basic data science functionality? Is this used by actual practitioners?
I wish HN had a way to save a story without upvoting it or showing it publicly on your profile (the "favorite" feature implemented right now), like Reddit's "Save". Many times I'm interested in something to check out later but it's not something worth upvoting (like this story, based on other comments) and I want my interests to stay private.
This issue is better solved externally - using a bookmark manager. It would allow you to have all your "read later" links in one place rather than being scattered over different websites. Personally I use Safari's reading list feature for that.
Quite a few people were unhappy when twitter renamed "favorite" to "like" because they had used it as a bookmark and did not want to imply advocacy. Seems like both intents could be supported fairly easily.
No, Watch emails you a bunch. Stars just show up in a list so you can find it later. That being said, public bookmarks always seemed weird to me. Why not just actually bookmark it with your browser? Not that it matters.
You may want to revise your judgement, GitHub added support to watch on "custom events", such as: new issues, new PRs, new releases, etc. You might want to try again.
Thank you! I've seen this language/extension/library pop up a few times and I don't see, even remotely, how it could displace the Python data science stack. The biggest competitor to Python in this space, IMO, is Julia. Go+ seems light-years behind, and heading in the wrong direction entirely.
My point is that I enjoy the Python stack, and I'm seriously considering Julia on future projects; I'm not giving R the same consideration. Python vs. R is almost a matter of taste IMO. I vastly prefer Python to R for data science. That's not to throw shade at R. Like you suggested, the Python stack owes R everything.
Apart from Gonum[1] numerical libraries, I haven't found specific data science related Go libraries in my search for it for some hobby projects when compared to Python ecosystem.
Interestingly Prose[2] A Go library for text processing yielded better results for named-entity extraction when compared to NLTK in my tests in terms of accuracy and obviously performance.
Perhaps Go is not being applied enough in the Data Science/ML and for fields where it's applied (Network) Math in the standard library seems to be sufficient.
How is list comprehension a data science primitive? How did this get over 4,000 stars on GitHub with a glaring lack of basic data science functionality? Is this used by actual practitioners?