Uh, I was gonna edit my post, but somehow hid it. Let me add more here.
If you got an admission from top programs like those two, I assume you already have fair amount of statistics education from undergrad?
I will assume that, correct me if I'm wrong.
Consider a business undergrad going for an MBA.
Total waste of time in terms of gaining additional knowledge, but people still do that for the value of networking.
Yes, top tier MBA such as HBS will give you a splendid network that can far outweigh any form of academic knowledge.
You are basically doing the same thing with this guy, but without the networking advantage.
As Pleaaa put it nicely, the role you will get as a master's student in the industry will be mostly validation or programming - which will not be far different from the role that you can get after a BS. PhD belongs to another league.
The best path you can take, in my opinion, is to defer UCLA admission, work in the industry for 1 or 2 years to have some practical experience, then go for a PhD.
Data science is a very practical field. Having an industry experience will make a big difference in terms of your future choices and paths.
(But... to be honest, in the end of the day, a smart guy like you can never go wrong, whichever path you take. Congratulations!)