# Ph.D. in Statistics Profile Evaluation for Fall 2022

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Background: I have a math BS with 2+ years experience in actuarial work - looking to get my hands on something more interesting. I had zero exposure to grad school, so I posted here to get some advice that led me to applying this cycle.  Please read that Reddit post for better background.

Undergrad Institution: Large R1 Public State University
Major(s): Mathematics (Actuarial Science concentration)
Minor: Statistics, Information Science and Technology
GPA: 3.98
Type of Student: Domestic white male

GRE: Not taken yet, first practice scores are Q: 168/V: 168/W: NA

Coursework
Math/ Statistics: Calc I, II, III: A, Probability theory: A, Real Analysis: A-, Proofs: A, Mathematical stats: A, Regression: A, Time series & forecasting: A, Computational math: A, Number theory: A, Linear Algebra: A-, Linear programming: A, Stochastic processes: A, Actuarial Math 1: A, Actuarial Math 2: A, Stochastic modeling & queue theory: A, Statistical programming: A

Research experiences: Currently assisting industry group research project

Working experiences: Assumptions setting actuarial work (statistical modelling) for fortune 500 insurance companies, and various automation projects. I have very specific research questions in mind that would have large impacts in industry that I will mention on SOP (current industry standard assumptions are crude and demonstrably false).

Skills
Programming languages: Python, SQL, R, VBA, SAS

Teaching experience
- TA'd probability theory for 3 semesters (lectures, hw, tests, everything, basically)

Letters of recommendation: 3 in total
Two college profs and one industry manager. All will be very positive, 2 will be strong.

Misc Activities (but Relevant):
1. First place out of 50 in college Datafest (team captain)
2. Third place out of 200 in college Hackathon (team captain)

Schools

I'm interested in pure Stats Phd in stochastic modelling, spatial stats, and dimension reduction. It seems pretty much all schools have some combination of these. So... whereabouts should I be applying in terms of reach, target and safety schools? I am having a hard time pinning down my competitiveness -- I have great grades and scores, some great stories to tell in the SOP, but no grad-level coursework, no formal academic research experience and my LOR will be a little dated (haven't kept in touch with profs in 2 years, but they will remember me).

Thank you very much for your help!

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Your profile looks pretty strong, and I think you should be very much in the discussion for schools like University of Washington, Duke, and University of Michigan -- provided you can get strong letters of recommendation that can say a bit more than that you are a strong student (i.e. that you have strong research potential). I am not sure if a letter of recommendation from an industry manager will be helpful unless this manager has a PhD in Statistics, Math or a related field and can speak to your research potential. If this is not the case, then I would recommend going with a third professor for your third LOR instead of your industry manager. As long as two of of your letter writers can speak to your research potential, a third "acceptable" one that mainly emphasizes that you are a good math student should be sufficient.

I would recommend applying to a bunch of schools in the range of 16-30 in USNWR (e.g. Minnesota, Texas A&M, North Carolina State, University of Wisconsin-Madison... I think you would be able to get into several of those) and then applying to maybe an additional 4-5 schools ranked above those. Schools like Stanford, UChicago, and the Ivies may be tough for you to get into, since you don't have academic research experience or graduate-level coursework (these schools seem to prefer applicants who have done some graduate-level coursework in math/stat and who have already taken the equivalent of the Casella & Berger sequence of Masters-level mathematical statistics). But you might get lucky, and I could see a school like UW, Duke, or Michigan choosing to admit you. At UMich and Washington, the first-year PhD students start out with Casella & Berger.

Edited by Stat Assistant Professor

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