[layman51]

> Data science effectively rebranded statistics but removed the requirement of deep statistical knowledge to allow people to get by with a cursory understanding of how to get some python library to spit out a result.

That's a good way of putting it. I remember in my first calculus-based probability+statistics class in college, I felt incredibly challenged by the theory. I wondered why there are so many probability distributions out there, why the standard stats formulas look like they do, what "kernel density estimation" even is, etc.

On the other hand, my data science course did include some theory, but a big part of it was also learning how to type the right commands in R to perform the "featured analysis of the week" on a sample data set. Something about these lab exercises felt off because it felt more like training rather than education. The professor expressed something along the lines that if we wanted to go far with this in the future, he would expect us to design the algorithms behind the function calls. I think the analogy he used was "baking a cake from scratch rather than buying a ready made one at the store."