discreature

Hello all! I created a Discord server for applicants to Stats graduate programs a couple years back, but I'm only actually applying in this coming cycle. It's been dead for a while, so I thought it would be appropriate to revive it here. Send me a DM if you're interested in joining, as I would not like to post the link publicly in case of bots.

Hello all! I created a Discord server for applicants to Stats graduate programs a couple years back, but I'm only actually applying in this coming cycle. It's been dead for a while, so I thought it would be appropriate to revive it here. Send me a DM if you're interested in joining, as I would not like to post the link publicly in case of bots.

Pure probability research is generally pretty rare in statistics departments outside of a few departments.  For statistics research, people generally divide it up into applied research (answering questions), methodological research (creating new statistical methods) and theoretical (proving mathematical things about methods).  Most statisticians do some combination of more than one of these, and a large amount of the research you'd do in a PhD program would likely be some combination of methods and theory research, with maybe some applied work especially if you're in a biostatistics program.
For application areas, obviously statistics can be applied to almost anything so just look for applied areas that interest you - if you name it, you can probably find it.  For methods, there are too many areas to list - causal inference, high dimensional, Bayesian, survival analysis, functional data, spatial, networks, a million more.
The exact subdivisions aren't as important in statistics as in math because it's much easier to move between fields and work in multiple fields.   You seem to be getting a decent idea of what's available in a lot of departments - so find a department that has a few people who seem to be doing something interesting and don't worry too much about being boxed in.

I think that it's a great idea to get a letter of recommendation from the person who supervised your first author paper. A first author paper is a clear sign of "research potential," even if it's not what you end up doing your dissertation research in. As long as your LOR writer can explain your research contribution in their recommendation letter and convey clearly that you performed statistical analysis, it should make a positive impression. 
I say go with the Epidemiology professor for a recommendation letter. Try to get at least one letter from a math professor who can highlight your mathematical abilities, your coursework in math, etc. 

Totally agree with @Stat Assistant Professor, and I would add that taking some CS classes will also be useful. In particular, Object Oriented Programming and Data Structures / Algorithms will help you out a lot, even if they don't boost your application (they will make your dissertation life easier).

Calculus I-III, Linear Algebra, and Real Analysis are the bare minimum you need for most Statistics PhD programs. However, more math beyond the bare minimum is always helpful in boosting your application. So strong performance in classes like abstract algebra, number theory, and complex analysis are definitely viewed positively by adcoms, as they signal mathematical maturity even if these subjects are not directly applicable to statistics. I would thus encourage you to take more math to boost your application. The only upper division statistics classes that are very helpful for Statistics PhDs are Calculus-based probability and statistical inference (at the undergrad level). These might make the first-year Casella & Berger mathematical statistics sequence somewhat easier. 
Some of the more "relevant" upper division math classes to Statistics are numerical analysis, advanced (proof-based) linear algebra, and optimization.  

It's also important to remember the role that applicant self-selection plays in the process. Most applicants won't apply to every top 10 program, so each admissions committee only ranks a subset of the applicant pool. This actually helps a lot; there would indeed be significant noise if every admissions committee had to rank the top 100 applicants to statistics programs, but things become a lot more stable when programs only have to decide who to admit from among a smaller group. Consider, for example, a school that is ranked between #5 and #10 in the country. It might attract ~20 of the top 100 applicants (some don't apply below top 5 and others just aren't interested in that program for various reasons). Assuming it accepts ~30 students per year, it's likely that most of these top 100 applicants will be admitted, because they are being compared to applicants that aren't among the top 100. Even for top programs like Stanford, the same logic applies, except perhaps replacing "top 100" by "top 50" (since Stanford might perceive a meaningful difference between a top 50 and non-top 50 applicant).

Same with ranking schools :^)

Your assumption about biostatistics departments is generally not correct - admissions are generally handled at the department level.  Chicago statistics is the only school I know that explicitly mentions your profile will be reviewed by professors with similar research interests.
I don't think this situation you're imaging exists, because the problem of having a bunch of good candidates who are hard to distinguish is not true.  Even if there are 100 domestic applicants with high GPAs and perfect GREs, then you group them by the prestige of their undergrad, their research experiences, their letters  -- there are decisions to be made on the boundary, but if I showed you the profiles of Stanford and CMU's new class or Washington biostat vs Michigan biostat, you could probably guess which is which -- not everyone at a top 10 program is a genius, so there is lots of variation even at the top.
Secondly, there is no true ranking.   Some programs have different goals, cultures, and want to accept different types of applicants.  A ranking of applicants would need a define criteria (eg "most successful academic career ahead"), and those are different for different departments.  "will succeed in completing a PhD" is probably the most important criteria, and that would only produce a categorization, not a ranking.  But these are just irrational humans reading a set of documents and choosing which other humans they want to offer to come to school.  I don't see what you gain from conceptualizing a "true ranking" -- you have to be clear about what this means or measures or the idea is meaningless.  "Quantifying people as just a number" isn't just a moral issue because it could be dehumanizing or because it's hard to estimate -- these numbers literally do not exist in any meaningful way.
Most importantly, this is not how admissions works, so even if the applicants are hard to distinguish in this ranking system, the school doesn't need to stress too much about ranking the top 20, because of self-assortment you can send 30 offers, 30 waitlists, and let the students self-assort.  Why stress differentiating between applicants 3 and 4 if they're both going to go to a different school?
Edit: To be clear, I enjoyed thinking about this and it brings up interesting issues/gets the wheels churning about how to think of the problem in a statistical way, but I just don't think it has much basis in the real world.

I don't think it will be a factor in admissions, but it could be a factor for funding. One of the grad coordinators said my work experience (no peer reviewed papers published) helped me get a fellowship offer.

Admissions is mostly based on grades, test scores, and letters of recommendation.  Industry experience can indicate some programming experience and some general life experience that I think could be viewed positively, but it's not going to be a major factor.  But, if you're not in school taking hard math classes or doing research that will get you papers and letters of recommendation, what is there to lose?  Go make some money.

Hey full disclosure; I'm not yet a grad student so I don't know how much you should value what I have to say. But IMO, abstract algebra isn't too important for the subject test. It's never more than a few questions - you can always get >90th percentile by doing well on the other questions. So I definitely wouldn't recommend taking abstract algebra (or number theory) just for the sake of doing well on the subject test. If you have some personal interest in the classes, that's different, but those classes aren't particularly relevant for statistics unless you do something specialized like algebraic statistics.
Between the others, it sounds like you could spread them out between fall 2020 and fall 2021. I think you'd get more out of functional analysis if you have a better measure theory background first, but I don't think the ordering is particularly important.
Just my $.02!

After you updated your post with your info, I would have to agree with the posters above that your profile is very strong. You could probably apply to all top 10 programs, and I'm sure you would get into at least a few of them. UC Berkeley and UW definitely seem plausible, as does Stanford if you can score well on the Math Subject GRE. If you are more flexible about your geographical preferences, you could probably get into really good schools on the east coast or midwest as well. 

Domestic URM with an A in real analysis from a top 3 department -- you'll be in great shape assuming you do reasonably well on GRE.  If you applied today, I think top 10 is possible, definitely top 20s, with maybe some safeties in top 40.  Get some research experience so you can get some good letters, keep it up with the math grades, and I think you'll be in great shape to get into top 10 programs by the time you apply. Don't worry about the early grades at all.
To answer your question, the school you go to matters in the sense that your grades will be taken more seriously, and your letters will come from more known people.

- If you have not already taken it yet, take real analysis. And prioritize taking math classes rather than undergrad stat courses. Get A's in your math clases.
- Score well on the math section of the general GRE - ideally 164+. Unless you are planning to apply to Stanford or a school that "strongly recommends it," you don't need to worry about the Subject GRE.
- It seems like research experience is becoming more common for applicants to Stat PhD programs, so try to get some if you can.

Not the most inventive advice but I've definitely had more responsiveness when a prof that I know introduces me to them. Over email this is as easy as them just sending them an email with a brief intro and CCing you. So maybe you know someone form your uni that knows at least some of them? Best of luck

Hi all. Currently I'm a second-year undergrad who is interested in applying for Stats Ph.D programs my senior year. Overall, I'm relatively on track in terms of taking courses, getting involved in research, and such. I have one potential glaring hole though, my first year the transition to college was a bit tough, and I got some Bs in relevant courses. Namely Calc II and Probability. Almost all Stats programs emphasize these two courses as being important. Retaking these courses would be possible but I don't think it's worth it since it's just a B, but how can I show future grad schools that I'm not actually weak in those areas?
 
I currently plan to take mathematical statistics (which has probability as a strong prereq) and perform well there, as well as take stochastic processes and perform well, but what courses could I take/things could I do to make up for my Calc II grade? Is high performance on the GRE (or Math Subject GRE) and higher level math courses enough, or is there something else? Or perhaps I'm overthinking this.

At least in physics, grad schools usually care more about your grades in your upper-level coursework than your freshman year. I would guess it's similar for statistics, since they probably care a lot more about whether or not you can construct a statistically rigorous sampling algorithm than whether or not you can prove a Gaussian integrates to the square root of pi.
In other words, taking some upper-level coursework should be sufficient. If your school doesn't already require it, multivariable calculus would be a pretty good way to prove that you learned calc II well enough.