2020 Statistics profile

[Cleepople]

Hey guys, I'm currently wrapping up my undergrad and I was thinking about applying to some stats MS/PhD programs in a year or so after I do some traveling but I'm not sure how to gauge my chances. I don't have much knowledge on what these MS/Ph.D. programs are looking so I was hoping you guys could share some insight. I've also scheduled a meeting with my advisor soon to learn more. 

Undergrad Institution: University of California, Berkeley
Majors: B.S. Mathematics & Statistics
Minors: Computer Science, Data Science
GPA: 3.77/4.00

Type of Student: Domestic Male

 

GRE General Test:
Q: 170 
V: 169
W: 5
GRE Subject Test in Mathematics:
M: 87%

 

Programs Applying: Statistics

 

Research Experience: Two research positions in AI/ML labs under well-known CS researchers. 1 research position in applied statistics under UCB professor
Awards/Honors/Recognitions: Some awards/honors related to athletics and my fraternity but nothing relevant.
Pertinent Activities or Jobs: 1 ML internship at a startup, 1 SWE internship at a big tech company (Google/Facebook/Amazon/Microsoft), 1 internship as a quant researcher at a quantitative trading firm. I also have my own sports analytics blog. Lots of personal CS projects.

Letters of Recommendation: 2 from well-known ML/AI researchers at Berkeley, 1 from stats professor I've been doing research for, 1 from stats professor who taught my grad level probability theory class. All should be pretty good.

Relevant Grades:  Single Variable Calculus, Multivariable Calculus, Linear Algebra & Differential Equations, Proof-based Linear Algebra, Real Analysis, Numerical Analysis, Concepts of Probability, Mathematical Models for Optimization, Stochastic Processes, Introduction to Time Series, Probability Theory (Grad-level Class), Introduction to Data Science, Principles & Techniques of Data Science, Algorithms, Artificial Intelligence, Machine Learning (Grad level class taught as an undergrad upper div at Cal)

A's in all except A- in Numerical Analysis, Mathematical Models for Optimization, Machine Learning, Algorithms, Introduction to Data Science

Coding Skills: Python, Java, C++, R

 

PhD's programs I'm thinking of applying to: Stanford, Berk, UW, Michigan (I will add more reaches and safeties as I research departments more)

Masters Programs: Stanford, Berkeley

 

My main concerns are whether or not my math background is sufficient (I have no clue how much math these programs like to see), if my lack of dedicated stats research experience will hinder me and if my lack of pertinent honors tanks my chances at top programs. Any recommendations on where else I should apply to are greatly appreciated too!

Thanks so much for reading this!

Edited December 18, 2018 by Cleepople

[Stat Assistant Professor]

I think you have a very strong profile, with a good GPA from Berkeley, research experience, strong grades in math classes (including an A in graduate-level probability! That's not an easy class!), and a high score on the Subject GRE. Research in AI is definitely relevant to Statistics. I wouldn't be too concerned about awards.

Assuming you have very strong recommendation letters, I anticipate you will be able to get into a program like University of Washington or University of Michigan. Berkeley isn't out of the question either. Stanford is a difficult one for most to crack. Do you have any geographical preference? I think you have very solid chances at schools like UMN,  University of Wisconsin, Texas A&M, and Penn State. And you could afford to try a few other top tier programs like Carnegie Mellon, Penn Wharton, or Columbia. 

Edited December 18, 2018 by Stat PhD Now Postdoc

[Cleepople]

5 hours ago, Stat PhD Now Postdoc said:

I think you have a very strong profile, with a good GPA from Berkeley, research experience, strong grades in math classes (including an A in graduate-level probability! That's not an easy class!), and a high score on the Subject GRE. Research in AI is definitely relevant to Statistics. I wouldn't be too concerned about awards.

Assuming you have very strong recommendation letters, I anticipate you will be able to get into a program like University of Washington or University of Michigan. Berkeley isn't out of the question either. Stanford is a difficult one for most to crack. Do you have any geographical preference? I think you have very solid chances at schools like UMN,  University of Wisconsin, Texas A&M, and Penn State. And you could afford to try a few other top tier programs like Carnegie Mellon, Penn Wharton, or Columbia. 

Thanks so much! Geographically my first choice would be to stay in the Bay Area, but I'm also totally happy with states like Minnesota/Michigan/Wisconsin/Illinois or going out on the East Coast. Given my preference for the Bay Area, is there anything I can do to strengthen my profile for Berkeley or Stanford? It's totally fine if those two schools are way out of reach for me given my profile, I'm just trying to understand where I stand. 

[Stat Assistant Professor]

34 minutes ago, Cleepople said:

Thanks so much! Geographically my first choice would be to stay in the Bay Area, but I'm also totally happy with states like Minnesota/Michigan/Wisconsin/Illinois or going out on the East Coast. Given my preference for the Bay Area, is there anything I can do to strengthen my profile for Berkeley or Stanford? It's totally fine if those two schools are way out of reach for me given my profile, I'm just trying to understand where I stand. 

Just make sure your letters of recommendation are very strong. Stanford is tough for just about anybody, so it's hard to say if you are a "sure" thing there. But I think you have a reasonably good chance for Berkeley. I would recommend taking some more advanced/graduate-level math classes, since the UCB Statistics PhD application asks you to submit a list of your advanced math/statistics courses, course descriptions, and textbook used. Since you got an A in graduate-level probability, you should be able to do well in a graduate-level math courses like measure theory and functional analysis -- looking at the UCB Math Course descriptions, this appears to be the sequence "Math 202: Introduction to Topology and Analysis." Classes like those are sure to make your application stand out.

Starting in the fall of next year, I would also recommend taking the Masters level mathematical statistics sequence (looks like this is STAT201A-STAT201B at Berkeley). It seems like you should be able to perform well, given your past performance.

Edited December 18, 2018 by Stat PhD Now Postdoc

[Cleepople]

12 hours ago, Stat PhD Now Postdoc said:

Just make sure your letters of recommendation are very strong. Stanford is tough for just about anybody, so it's hard to say if you are a "sure" thing there. But I think you have a reasonably good chance for Berkeley. I would recommend taking some more advanced/graduate-level math classes, since the UCB Statistics PhD application asks you to submit a list of your advanced math/statistics courses, course descriptions, and textbook used. Since you got an A in graduate-level probability, you should be able to do well in a graduate-level math courses like measure theory and functional analysis -- looking at the UCB Math Course descriptions, this appears to be the sequence "Math 202: Introduction to Topology and Analysis." Classes like those are sure to make your application stand out.

Starting in the fall of next year, I would also recommend taking the Masters level mathematical statistics sequence (looks like this is STAT201A-STAT201B at Berkeley). It seems like you should be able to perform well, given your past performance.

Thank you so much for this advice. I will definitely take those two sequences next year. Also yes, my letters of rec should be very good.