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.
Data science is an overloaded term, but even so there are some salient differences between it and statistics.
Data science more closely related to "statistical learning" and the knowledge required overlaps with but looks quite different with that of conventional statistics.
An easy way to get a sense of the difference is to compare the table of contents of a book like ISL (PDF free) [1] to the undergraduate curriculum of a statistics program. You'll find that that the focus and indeed culture of data science is really quite different from that of statistics.
Leo Breiman wrote about this in his paper "Statistical Modeling: the Two Cultures" [2]. Conventional statistics belongs to one culture, and statistical learning/data science sort of veers toward to the other (though not completely).
Much has been made about how "data science" is just statistics dressed up to look new, but I'm not convinced this is true. I'm also not convinced that pure statisticians have the right training to be data scientists -- additional training and mindset changes are needed. The reverse is also true: most data scientists lack the rigor and epistemological training to be statisticians.
Indeed, perhaps applied statistics or even data analysis.
It has always felt stupid calling myself a data scientist, but the term statistician has certain connotations that are not always relevant for the corporate context.
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.