I feel like lists of resources are OK, but with something like data science, which has its own branches and specialities, it would be good to have some kind of stack ranking of topics and information beyond just 'start here'. That way, a reader gets more and more conversant with the different ideas being thrown around.
Also, I don't have a list of these handy, but I've found long annotated notebooks/blog posts of worked data science examples very helpful for refreshing my memory on applied techniques. http://derandomized.com/ is a great example, maybe other HN readers have some favourites we could add.
The list is for beginners.Ofcourse data science is a huge domain and you can't actually make a roadmap to be a master in this field but I am sure this list will help people to get basic understanding of what to read and how to workout things.
I think the list is more helpful for someone a step or two beyond beginner. A true beginner is going to look at that and be scared. Once they've read a few introductory things, they'll be able to go back and make better sense of it, for sure. (I showed this to a friend of mine who's interested in learning data science and that was his reaction, so I am generalising, but I think it's a fair generalisation.)
Also, I don't have a list of these handy, but I've found long annotated notebooks/blog posts of worked data science examples very helpful for refreshing my memory on applied techniques. http://derandomized.com/ is a great example, maybe other HN readers have some favourites we could add.