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Hi everyone. I’m in a little bit of a pickle in choosing some courses for this year. A little background: I’m finishing up a MS Stat this year, and was hoping to apply for the Fall 2019 Stat PhD cycle. I have the opportunity to take a grad level Real Analysis/Measure Theory sequence, or a Bayesian Statistics sequence, but cannot do both. My question is, with some mathematical background already (I completed a year of analysis in undergrad and did fine), would it be a wise choice to opt for the Bayesian courses? I haven’t yet taken a Bayesian course and have only scratched the surface in a theory course which mainly focused on the frequentist perspective. From what I’ve read, it seems like something that I might really enjoy doing more of, especially in a doctoral program (but I can’t really know unless I do it). However, I understand that perhaps my application would be stronger with measure theory under my belt before pursuing a PhD. This being said, the grade likely wouldn’t be posted until after applications are due, but only slightly. If anyone has insight into my predicament or a similar experience, please don’t hesitate to chime in. Thanks.

Hi everyone, just another potential applicant seeking a little advice. I welcome and appreciate any and all insight, recommendations, criticisms, questions, etc. Undergrad Institution: Large State school Major: Mathematics Minor: Chemistry GPA: 3.28 (Major 3.68) Grad Institution: Large State school Major: Statistics GPA: 4.0 (currently) GRE General Test: (might retake, was falling asleep during parts of test) V: 160 Q: 168 W: 3.5 Programs Applying: PhD in Statistics (maybe Biostat?) Research Experience: Not really any research per se, but a couple of projects: one with a professor of journalism working on media framing of Islam in US, kind of a statistical linguistics project (article was not published with work I did, apparently referee was an Islamophobe, who really knows), and one outside of school with an actuary working on some demographic analysis for his business. Letters of Recommendation: One from undergrad prof who supervised my projects, two from Grad program who are well-acquainted with my work/study habits. Relevant Course Work: Pre-dropout: (rarely attended classes) Calc 1 - 3 (B, A, B+), Linear Algebra I, II (B-, B+), Limits and Infinite Series (B+) Post-dropout: ODEs (B+), PDEs (A), Complex Analysis (A), Intro to Real Analysis (A), Real Analysis I (A-), Statistical Methods (A-), Mathematical Biology (A), Mathematical Statistics (A), Probability (A), Abstract Algebra I, II (A, B), Regression Analysis (A), Time Series Analysis (A), Statistical Computing (A) Grad Courses: Mathematical Statistics I, II (A’s), Theory of Linear Models (A), GIS and Spatial Analysis (A) Future Grad Courses: Real Analysis/Measure Theory, and a handful of other statistics courses Computing Skills: R, C++, ArcGIS, QGIS Teaching Experience: Taught a year of undergraduate mathematics, will teach applied statistics this year Applying to: Minnesota (Advisor attended) Wisconsin Colorado State (Two profs attended) Oregon State UCSC (looks like they do cool research here) UC Davis I know the math grades/GPA don’t look so great, but I was going through some medical/personal issues and feel I made quite a rebound after returning to school. The low LA grades I would hope are made up for in some of the more advanced math classes as well as my graduate coursework (I feel like I really got the chance to learn linear algebra properly when I took the Linear Models course). I decided to try a Master’s to see if I could handle Grad level coursework, and it seemed like it would dovetail nicely into PhD programs if I enjoyed the atmosphere. I am much more comfortable in theoretical statistics (hypothesis testing, confidence intervals, distribution theory), but am also interested in longitudinal data, as well as mixed models, so maybe throwing in a biostatistics program here or there might be wise. It’s hard to say at the moment, but I am just enjoying learning and exploring new material. I haven’t had much exposure to the Bayesian paradigm, but feel I would really enjoy getting to explore it in a spatial/temporal/ecological setting. This year I will participate in a consulting lab, so hopefully this will help more precisely define my research interests. Thanks!