noskillz

@Casorati and @StatsG0d thanks for that. Do then think that instead applying to UIUC, Texas A & M and Purdue would be better? As @Casorati said, I should be aiming above McGill and I believe this schools would satisfy that while also being within my reach (maybe).

Thanks a lot for all the advice. In case any one is wondering I have reduced my final list choice to McGill, Waterloo, Toronto, UBC, Penn State, Michigan, Minnesota, UNC, Columbia, Rutgers, Northwestern and UCSD (Stats PhD for all). I decided against going in to bio stats because I just didn't see myself really enjoying being in bio-stats for the next 30 years of my life. I also added Rutgers and Northwestern , since they are in 30-50 range and seem like good schools within my range which I would also enjoy going to. Especially Rutgers, since it has a decently sized stats department from what I saw on their website.

@StatsG0d, by waste you mean totally unachievable or programs are not good? Thanks for your input. Yeah I thought about emory. Will look into it.

@StatsG0d Thanks for your response. My current supervisor actually has a contact in McGill Stats and said that the contact was looking for a phd student so that's why I listed it as a safety. In terms of a little bit more details, I would be applying to: Bios: Minnesota, Michigan, Brown, Duke, UNC, McGill Stats: Michigan, UNC, Columbia, Duke, Minnesota, Yale, Toronto, Pittsburgh, UBC, Brown, McGill, SFU I was also thinking of adding UCSD stats as an option as well (I wouldnt mind applying to UCSD master's in applied math and then take that as a stepping stone). There are also some recent phd programs in data science, like in NYU and UCSD also has on. But I'm a bit sceptical as to how strong of a research background some of these 'new' programs will give to their students. Thanks!

Hi guys, I just wanted to get some insights into whether or not my school selections are proper. I'm Applying for Statistics/Biostatistics Phd programs for Fall 2021. Unfortunately my profile is really quite a mixed bag. Overall my undergraduate grades where quite underwhelming because I absolutely hated being in school until my third year (during which time there was also serious illness in my family). Things turned around for me in my last year when I took a research course which I overwhelmingly enjoyed and my supervisor encouraged me to just try my luck at a graduate program. I was extremely lucky to have gotten into the graduate master's program in the same school as my undergrad. And, not that this means much given the profiles of other people on this site, I did phenomenal in my Msc in stats program. And that too in taking really difficult math classes. My goal during my master's was to build up on everything that I messed up on during my undergrad. I did about 3 courses more than what was needed for the degree requirement on top of doing a thesis, which we are now working on to submit for publication. I have also built up a decent research profile and did extra research on top of my thesis work with one of my supervisors and one of my course instructors. My three LoR's will all be from the professors that I have worked with and all three are fairly well known in their respective fields (2 for Bayesian statistics and 1 in econometrics). I'm just thinking about if my school choices are correct and which schools I could/should realistically aim for. I know my undergrad fuckup will follow me for a while especially into the PhD application, but if there's anyone with some advice on how to mitigate that, or help me adjust my expectations down to a realistic level that would be very helpful. Undergrad: Mathematics (Top 3 ranked Canadian Institute.) Gpa: 3.23, my grades are quite a mixed bag. I got an A in measure theory, A real analysis, A ODE, A Complex analysis, A- probability, D in linear Algebra, B- in Intro to Statistics, B- in Statistical methods (graduate courses), A in research course, B in regression, B- topology, C in GLM. Master's (Thesis based): Statistics (Same institute) Gpa: 3.93, Advanced Probability (using measure theory) A-, Advanced GLM A-, Advanced Real Analysis A, Convex Analysis A, Bayesian Inference A, Financial econometrics A, Time Series analysis A, Advanced Computational Methods A; PDEs A, Asymptotic and finite sample theory A, Numerical Analysis A, Functional Analysis A (hoping to use this to make up for the D in lin alg). Took these last 4 graduate courses after I finished my master's. Gre: 165Q 164V 4.5A (the verbal score was a SHOCK to me as well!) As you can see even after finishing my master's I have tried to compensate for my poor undergraduate courses by taking graduate courses that will build up my fundamental math knowledge and have done really well in them. All the while being involved as a research assistant with my professor. I also have a strong teaching experience having done 4 ta-ships during my masters and was super lucky to actually been able to teach a first year math course in the immediate aftermath of finishing my masters. Applying to: Reaches: Michigan, UNC, Columbia, Duke, Minnesota, Yale, Toronto, Pittsburgh, UBC, Brown (honestly just have a felling won't get into any of these, my supervisor thinks I can get in but I don't think so.) Safety: Simon Fraser, McGill Let me know if I'm aiming too high and if so what are more realistic options. Thanks a lot. And good luck to everyone with their applications and if you're still in school with your midterms/exams. Thanks.