There are some differences between stats and data science.
At least initially, DS was a lot about machine learning. While those methods may be statistical, it was the computer science field that drove and embraced the ML revolution. Currently, it’s mostly ML engineers who make the impact (deploy) ML and these are mostly CS folks. Statisticians still can’t code themselves out of a box (2013 MS Statistics here from top school)
Also, there has been a lot of innovation in managing data at scale (tools, infra , etc) This, again, has been done by engineers not statisticians. But this is still related to the science of data.
So the difference between the new (data science) and the old (stats) is about culture and about some of the methods for dealing with data at “scale”.
In other words, statistics is just a part of data science, but not the whole.
At least initially, DS was a lot about machine learning. While those methods may be statistical, it was the computer science field that drove and embraced the ML revolution. Currently, it’s mostly ML engineers who make the impact (deploy) ML and these are mostly CS folks. Statisticians still can’t code themselves out of a box (2013 MS Statistics here from top school)
Also, there has been a lot of innovation in managing data at scale (tools, infra , etc) This, again, has been done by engineers not statisticians. But this is still related to the science of data.
So the difference between the new (data science) and the old (stats) is about culture and about some of the methods for dealing with data at “scale”.
In other words, statistics is just a part of data science, but not the whole.