trynagetby

I think the point of the separation is that Biostatistics and Statistics programs are pretty different worlds and the comparison between departments in the two fields was not particularly productive.

Current Duke PhD student here. Last year at least 2 people got off the waitlist.

applied statistics masters programs are not very competitive. If you get a good GRE score you can get into a few applied statistics professional/terminal masters. You might not be able to get into Stanford MS&Engineering, but you can definitely get into a few decent ones.

If you're worried about Duke being too Bayesian, I'd consider the fact that Duke's becoming significantly less Bayesian recently. Fan Li does causal inference research that's pretty non-bayesian, Alex Volfovsky works with Cynthia Rudin in the CS department on pretty straight up ML research, Jason Xu does mainly ML, Eric Laber (Susan Murhpy's student) does Susan Murphy type stuff. So if you're not a hardcore Bayesian there's still a lot of flexibility.

I think your list is good with CMU will be the toughest nut to crack there. If you do well in Real Analysis, I think you'd be a serious consideration anywhere UW and below on the U.S News rankings (not to say you shouldn't apply higher, esp given diversity considerations). Also if you're seriously interested in Bayesian stuff, the obvious question is why not apply to Duke/UT Austin?

If you look at the number of applicants for schools that release data for previous years (Duke, Cornell for example) the number of applicants in the 2021-2022 admission cycle was very much within normal variation. So if the beginning of the pandemic didn't budge numbers too much, I don't think that this years numbers will depart from previous years very much either (aka there should not be a flood of applicants this year). Anecdotally while there are people who delayed applications because of Covid, in my experience there are also people moved their application up a year for various reasons (I did as well as some other people I know). So it's kind of a wash.

I think biology courses for biostat is borderline completely irrelevant. With regards to going into non-bio-related fields I don't think it matters that much if you plan it right. To be a finance quant at like a prop shot (Jane Street/Optiver/SIG) they literally only care about how well you ace the hard probability quant interviews. For other finance positions the name brand of the institution matters more. But I think doing research in related field (time series, stochastic procceses, applied probability) does help a lot, which could be slightly more difficult at a biostat department. For tech/institutional research it depends on what you research. If you're interested in causal inference and want to do that in industry biostat programs should be great to get top industry research jobs. But if you end up resarching very niche methods for flow cytometry or phylogentics or something it'll be difficult to get anything beyond your generic data science job at a tech company (which still pays lucratively ofc). Ofc if you're researching niche methods for flow cytometry or phylogentics you'd likely be working at an actual pharma/biotech company lol

If the recommender writer was on the admission committee for Wharton Stat I'd listen to him. But if he was on the admission committee for Penn finance I'd stick with the prevailing wisdom that Analysis I is probably the first grade that Stat AdCom look for on the transcript. I've seen people with diversity demographics or nontraditional/funky profiles get by with an eh grade with Real Analysis (or if they're EE or Physics Undergrad) but you definitely don't fall into that category. Good recommendations will go very far tho. I'd definitely encourage you to apply all the schools you'd want to go to if you can afford them, you never know what could happen. I definitely slightly regret not applying to enough *reach schools during my cycle. If you're legitimately interested in Biostat, I'd apply to like all the top ones where you think you'd be happy because I think you have a chance at all of them. Harvard, JHU, UW will be hard ofc but it wouldn't be impossible. The other good schools would be like Columbia, UNC, Umich, Penn, Duke etc...

Those schools are probably going to be VERY difficult. Still recommend applying to them if your rec writers are okay with them and you have the money. Additionally the jump from the Chicago/CMU/Wharton/UWashington/Columbia competitiveness tier to penn biostat tier is pretty dramatic. I'd add in schools like NCSU, UCLA, Winsconsin, UNC Biostat, Mich Biostat. IMO you have a much better shot at top Biostat programs than Stat programs given your good performance in stat classes. Also important note is if the graduate classes you mention are PhD or Masters. Masters graduate statistics classes are fairly meaningless in stat PhD admissions (as in they will not alleviate at all lack of advanced math). PhD graduate classes are a big value add (in that they can alleviate lack of advanced math). You should check out my profile to get an upperbound of what would be realistic. I was dinged at CMU and Waitlisted at UT Austin despite having more math. Unfortunately as an International Asian Male you're in the most competitive possible demographic category. If you don't get at least an A- in real analysis Wharton, CMU, Yale, Columbia, and Chicago are likely out of reach. If you're feeling good about your calculus a 80%+ on the Math subject test GRE would be extremely helpful.

If you're domestic and not strapped for cash I'd advise adding schools on the upper end of rankings if you're interested in their programs (think Uwashington, Duke, etc...). This advice especially applies if you're very close with those two professors and expect good LOR. Edit: Not sure when all the application deadlines are so apologies if they have already passed.

There's nothing wrong with an LAC! I think your list is still reasonable , if pretty top heavy. The PhD courses definitely help a lot and I think that's a reason why you have a good shot at those programs, but generally the rule is the more math you have as an undergrad the better. Honestly, the biggest factor will be the strength of your recommendation letters. If you think your letters are top-notch your list is probably good. Take my advice with a grain of salt as I only applied to 2 biostat schools, and got into 1.

List seems pretty reasonable. Just posted to say that if you're interested in doing research/pursuing PhD afterwards DO NOT even apply Columbia Stat MS. Also your GRE is probably fine.

If you've had a lot of advanced mathematics background (Real Analysis I, II, Proof-Based Lin-Alg, + one more advanced class like Analysis of Algs, Abstract Algebra, Numerical analysis etc.. ) those schools are definitely all well within reach. If you've just had Real Analysis or even no Real Analysis you have a dece chance at all those schools but a clean reject from all of them is very well in the realm of possibility (I don't know anything about BU). Given your locational preference those schools pretty much cover the northeast. I'd throw in JHU too if Maryland isn't too south for you. Safe(r) schools would be like UNC, Umich, Duke. But those are pretty out your geographical preference.

I think you have a pretty good chance to get into them. I'm don't know much about those programs, but in general for domestic students the competitiveness of PhD programs falls drastically once you get out of the union of top 30 Stat Schools and top 30 general schools.

If you got A's in Real Analysis/Applied Analysis (which I'm guessing is like baby functional analysis?) I think you should apply to the range of NCSU/Wisconsin Madison/UIUC/Rice/Texas A&M and below. NCSU/Wisconsin might be a little of a reach but its pretty possible :). I'd also think about applying to programs like Umichigans Biostat Masters (which is funded) to give yourself a leg-up before going PhD. If Biostats is an option, UNC biostats and below should be a good chance. Good Luck!