Frequentist0114

For Phd programs, I think what worries me most is the B in graduate level probability theory as a non degree student. Covid got in the way at work and forced me into a situation where I couldn't study much...that, coupled with the fact that the class was graded harshly did not work out in my favor (most students, who were PhD math students got Bs looking at the class metrics on the exams). However, I understand that adcomms see Bs like they do Cs in undergrad. Is that not an immediate deal breaker? I'm not asking about the absolute top programs since my goal is not academia.

Hey, thanks for the quick reply! I'm definitely more inclined towards biostatistics and might consider applying straight to doctoral programs. Seeing a lot of the profiles on here made me think I stood no chance without a masters degree first, so this is reassuring. My school isn't known for grade deflation per say, but the average gpa was around 3.4-3.5, though the math/cs average gpa was probably much lower. I'm curious about the difference in chances between bios and stats. Why is that?

Hello, I've recently come across this forum, and wanted to get some opinions on my grad (MS, not PHD) school application plan for Statistics. Would be great to get some opinions. Undergrad Institution: Top 20 US News Major(s): Applied Math and Statistics Minor(s): None GPA: 3.36 (Not counting non degree coursework done after) Type of Student: Domestic Indian male GRE General Test: Q: 168V: 162W: 5.0 Courses (Taken at undergrad institution): Calc 2 (B+), Calc 3 (B), Lin Alg (C+), Diff eq (B), Intro to Proofs (A), Probability(B+), Mathematical Stats (A-), Real Analysis 1,2 (B,B), Lin Alg 2 (A-), Graduate Complex Analysis (B), Graduate Real Analysis (with measure theory) (A-), Numerical Analysis (A-), Time series (B), Advanced stats (B+), ML (B), Statistical Inference (A), GLM (B) Courses (Taken post grad as nondegree at various schools in my area): Measure Theoretic Probability (B) (Covid really messed this up for me here) Abstract Algebra (A) Graduate Lin Alg/ Matrix Analysis (A) Functional Analysis and Optimization Theory (A) Graduate Applied Bayesian Stats (A-) Graduate Applied Experimental Design(A) Research: One Capstone project as an undergraduate. Ended well, but it was a data analytics project with ML, not really statistics. Other Experiences: Been working as a business analyst since I graduated, so I am quite proficient with R/Python/SQL Letters of Rec: 2 from Professors, one from my boss I understand this is probably a weird profile. I was really undisciplined as an undergraduate and my grades aren't great. I decided I wanted to eventually end up at a PHD program my senior year during the capstone project and while taking graduate real analysis, because we spent some time looking at probability theory in that class and I was intrigued. I began taking non degree classes in an effort to ameliorate my mistakes as an undergrad. I realize PHD programs are ridiculously competitive, so I am planning to do a theoretical MS first, redeeming myself, and applying to PHD programs. I'm trying to look specifically at masters programs that are more theoretical and allow me to take some PHD level courses. After having done Casella and Berger as an undergrad, I'm not happy about basically doing that material again as a masters student, but if that's what it takes.... Schools I'm considering (all masters in statistics, or mathematics and statistics): US: UNC, Penn State, UCLA, UCSD, Florida State U, U Minnesota, U Georgia, University of Maryland (College Park and Baltimore), UIUC, Perdue, GA state, Texas State, Ohio State, U of Utah, TAMU Canada (Because money, but also mathematical rigor): Mcgill, UBC, Manitoba, Saskatchewan, Guelph, York, Western Ontario, Brock, Acadia, Queens