Are these schools too good for me? (2018 Masters in Statistics)

[daehyunk927]

I graduated from University of Washington, Seattle back in December 2016 and am currently working as a data analyst. I am planning to apply for Masters in Statistics this coming fall (to attend from 2018 Fall) and have tried to come up with a school list. 

My potential list is:

UC Berkeley

Harvard

U of Chicago

U of Washington

Carnegie Mellon

Duke

UW Madison

Columbia

Cornell

UCLA

UW Informatics

I did double majors in Computational Math from which I had all the required math courses plus some Computer Science courses, and also in Mechanical Engineering.

My cumulative GPA is 3.68 and my major GPA is around the same. I took GRE once without much preparation and got V:158(80%), Q:170(97%), W:3.5(42%). Should I take another GRE or forget it?

I have a couple of research projects in my experience: one from Mechanical Engineering department from which I evaluated computing performances and another from Electrical Engineering department from which I worked on machine learning. Both of the professors can write me good recommendation letters.

I know my GPA, GRE, and others are not good enough for top schools like Berkeley, Harvard, UChicago (which would be far reach), but I would like to ask about other schools like CM, Duke, UW Madison, and Columbia.

[jim94]

They look at your individual courses. If you did well on proof-based courses like real analysis, you would have a better shot at top 5 programs given your strong reputation of undergraduate institution. In general, you need good grades in math and stat courses. You did perfect on GRE Quant and your verbal and writing are more than enough so there is no need to worry about that. I would say working on recommendation letters because in these top schools so many applicants have similar profiles as yours and this is when recommendation letters differentiate you from others.