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UCLA vs UCSB vs Michigan Statistics Masters


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I have a background in Computer Engineering and seek an eventual PhD in Statistics. Knowing I lacked some of the math courses necessary for a PhD, I applied to PhD programs where you could be considered for the Master's program if not admitted to the PhD. I have been admitted to UCLA, UCSB, and University of Michigan for Master's, and am hoping to attend one which is going to prepare me best for continuing on to a PhD program. I know, for example, that Michigan's Applied Statistics Master's seems somewhat more catered to preparing students for industry, but I had also seen that a few of their students do in fact continue on to do a PhD.

I was curious if anyone could provide insight on these programs and inform me of the program's reputation in addition to how focused each Master's is on preparing a student for industry versus continuing on to a PhD.



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