[boredemployee]

>> would love to see a version of this that actually engages at a non-superficial level with topics such as database design, theory(ies) of data visualization, methods for storytelling with data, and interactive design.

I love these discussions and taxonomies in data science. So I have a few genuine/honest questions:

1) isn't what you said more "analytics" or "analytics engineering" oriented (which also and itself is a subtopic/subfield of data science) ?

2) I think that more and more people are trying to define what "data science" is, specially for marketing purposes, and then put it in a box, like any other science (i.e. chemistry - take an undergrad chemistry textbook and they will always cover the same topics). But since it isn't well defined yet, many different courses covers different algorithms/aspects of data science, so I think it end up looking superficial and hard to please everyone. Would you agree w/ that? For ex. I'm trying to find a good and in depth course that applies Data Science/Machine Learning in Big Data problems, but I just can't find any serious course covering it.

[laGrenouille]

I completely agree that it's an open question about what exactly constitutes data science and what should (or at least could) be covered in a standard introduction. For me, a fairly reasonable—though certainly not definitive—set of topics are five items listed on this course's syllabus. And that's what makes this so frustrating, personally. The instructors actually have a good proposal of what should be taught, but then just turn around and teach a classical course in statistical learning.