Stat Assistant Professor

Just "Intro to Real Analysis" (like convergence of sequences/series, continuity and convergence of functions, Riemann integral and differentiation) should suffice. I'm not sure what the distinction is between that and real analysis is -- unless the non-intro real analysis you're referring to is just a second semester of analysis with more advanced topics like metric space topology, Fourier analysis, etc. or a class of measure theory and Lebesgue integration. The latter is not needed for PhD admissions in Statistics. Anyway, if I were you, I would apply mainly to PhD programs in the top 30. Maybe pick 2-3 of the top 15 schools, a bunch in the range of 15-30, and 2 below that just to be safe.

There are not many funded Masters programs in Statistics, but there probably are a few if you search for them. Another possibility is to do a Masters in Mathematics with a Statistics concentration. In such a program, you could take a core of statistics classes -- two semesters of mathematical statistics at the level of Casella & Berger, regression, design of experiments, and linear models), along with some theoretical math classes like real analysis and advanced linear algebra. Masters programs in math may be more likely to be funded. I would look into those where you can do a Stat concentration, as well as the few funded Statistics MS programs that exist.

I don't think your profile is competitive, especially compared to other international students who have much more breadth and depth of math classes. The B in Linear Algebra is possibly concerning as well. I would recommend getting a Masters degree where you take real analysis AND advanced proof-based linear algebra (to partially atone for the B in lower division LA -- then have one of your LOR writers point out that you got an A in advanced linear algebra) AND maybe one other math class such as a second semester of real analysis. Then you can try your luck and apply to Statistics PhD programs. I think you could possibly get into some schools in the 41-80 range of USNWR. Higher than that is probably out of reach, given how competitive admissions is for international students these days.

Yes, if you were to apply to PhD programs, you could probably get into a number of good programs ranked 15-40 by USNWR... possibly top 15 as well, but admissions becomes a lot more competitive and even perfect 4.0 GPA students get rejected by those schools. Honestly, if there is even a remote chance that you want to do a PhD, I would just apply to PhD programs. Your profile is good enough for them, and if you decide not to go the PhD route, you can leave with a Masters. I will say, though, that if your goal is to teach at the college level, it may be preferable to get a PhD and then look for jobs at teaching-oriented schools (i.e. SLACs and non-doctoral granting universities).

With your perfect 4.0 from your undergrad, I anticipate that you will be admitted to every Statistics MS program you apply to. It's no big deal if you have been out of school for awhile. However, before you start your program, make sure to review Calculus I-III (you can skip any derivatives/integrals dealing with trigonometry, washer/disk methods, polar coordinates, arc length, and curvature) and maybe some linear algebra. The main things you need to review from Calc I-III are differentiation and integration, including things like the chain rule, u-substitution, integration by parts, partial derivatives, and multiple integration.

Having those types of connections may indeed be helpful. It could also depend on who is on the admissions committee. It just seems as though most of the "top" Statistics PhD programs are extremely selective about what domestic applicants they admit (to the point that some schools like Harvard and UPenn only have 1-3 domestic students enrolling each year). Some top programs like UC Berkeley, University of Washington, and Duke are a bit more flexible (in that they admit more Americans and not all from very prestigious undergrads).

Not nearly as important as the letters for Stat PhD programs. Since most Masters programs are unfunded, they will admit most students who meet the minimum coursework, GPA, and general GRE requirements.

I still think the likes of Columbia and Chicago might be tough. There was a combined Bachelor's/Master's American student at the school where I got my PhD from (a large flagship state university) who had done legitimate research on theoretical probability, had taken basically the entire Statistics PhD curriculum *and* a bunch of math PhD classes (measure theory, functional analysis, etc.), and he was still rejected from most of the top statistics PhD programs (he did end up at a very good program though -- one of the four "reaches" mentioned by the OP). I think the top schools are very competitive, and a sub-3.8 undergrad GPA from a less prestigious uni may have a more difficult time cracking schools like Columbia or Chicago. These schools don't accept many domestic students to begin with. That said, the OP does have a very good profile, and I would encourage them to apply to more schools in the top 20 if they can afford it.

With a 3.7+ from UC Berkeley and grades of B or better in your math/stat classes, I imagine you will not have any difficulty getting into most Statistics Masters programs.

Your profile looks pretty strong. I think Wisconsin, Penn State, UCLA, and Rice are definitely possible. Columbia, Michigan, UNC, and Washington might be slight reaches, as these are very competitive programs and there will be other domestic applicants to these programs who have better undergrad GPAs from more prestigious schools. However, your research experience is very solid, and your graduate school performance should instill enough confidence in your math abilities. If I were you, I would apply to 4 schools in the range of Wisconsin to Rice and then pick two schools from Columbia, Michigan, UNC, and Washington to apply to (of those, I think Washington and UNC are more likely to admit you).

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.

Oh, I was not aware of this at all. In any event, if the OP applies to programs at large state schools like TAMU, UMinnesota, etc., they might be able to get in. I have known people who got their PhDs from TAMU and UMN (and are now TT faculty at R1s) who did their undergrad at places like Central Michigan University or Southern Illinois University. I think **domestic** students from these regional schools with high GPAs stand a reasonable chance at most of the Stat PhD programs ranked 20-40 by USNWR.

I think your profile is good enough to get into UC Irvine. UCLA and UC Davis are also attainable for your profile. I don't think top 30-60 is a reach at all, and you may even be able to get into some top 20 schools as well (big state schools like Iowa State and TAMU seem like a good bet -- and possibly University of Minnesota as well). Consider taking one or two more upper division math classes this fall, and you should be good to go.

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.

If you are not interested in academia, then I don't see any reason not to graduate in four years if you can. The main advantages of taking a fifth (or sixth) year are: - more time to find a job - more time to get more publications on your CV. If you aren't interested in academia, then the second point is really moot. However, for those on this forum potentially interested in academic positions: if you have a good postdoc lined up in your fourth year, then I would still recommend finishing up more quickly, even if you don't have as many publications. The main consideration for taking an extra year is whether or not you will be much more competitive on the job market with that extra year. For example, I completed my PhD in four years, and by the time I graduated, I had only one publication and two papers submitted. However, I knew that if I stayed at my program a fifth year, I wouldn't have been able to make my CV significantly stronger, so I just graduated and went immediately to the postdoc. Meanwhile, one of my classmates could have also graduated in four years just like me, but he stayed for the fifth year so he could get an Annals of Statistics paper accepted and on his CV. This ultimately put him in a much stronger position in the academic job market and he was able to get a TT job at a very good department. So in my classmate's case, it was definitely advantageous to take the fifth year to ensure his CV was stronger.

For most Statistics or Biostatistics MS degrees, the most important things are, in order of importance: 1) Required coursework (Calc I-III and Linear Algebra), ideally with grades of B- or higher 2) GPA 3) GRE 4) everything else

I think it is definitely less competitive for Canadian students than for students from Asia. I think there are overall fewer students from Canada applying to Stat PhD students in the U.S. than there are from China and India (a lot of top Canadian students stay in Canada to get their PhD from UBC, UofT, Waterloo). There are also much fewer visa issues as well (for instance, Canadian citizens don't need F-1 visas to study in the U.S.). I know former students from McMaster, University of Mannitoba, and Acadia University who were admitted to PhD programs in Statistics at University of Washington, Yale, and Carnegie Mellon. So I imagine that someone with an excellent GPA From McMaster stands a good chance at very good Stat PhD programs in the U.S. (though the top 5 might be hard to break, as @cyberwulf mentioned).

Although OP is an international student, she does attend one of the top four universities in Canada and has a high GPA. And I believe applicants from the top schools in Canada are treated similarly as domestic applicants in the admissions process at a lot of U.S. Statistics programs (Canada's a bit different than elsewhere). OP: I think you have a good shot at a lot of very good statistics PhD programs (without the Masters), and so I concur with StatsG0d to apply directly to PhD programs. The top PhD programs are difficult for anyone to get into, so I would make sure to have a few "safer" options. But I wouldn't say that schools of the tier of University of Washington or Carnegie Mellon are out of the question for you.

The line between theoretical/methodological/applied is blurry to be sure. I would consider pure "theoreticians" to be those who mainly publish in places like Annals of Statistics, Bernoulli, Annals of Probability, etc., and I would consider "theoretical" research to be research which is mainly concerned with formalizing statistical analyses/procedures through mathematical theorems and proofs. If pressed, I think most statisticians in the U.S. would consider themselves methodologists, but they may tilt a bit more towards the theory side or towards the motivating applications.

Come to think of it, most of the Koreans I know who obtained their PhDs in the U.S. (and who had papers before they began their programs here) did have a Master's degree. It may have been the Masters program where they were able to obtain the necessary research experience and publications. I would consider doing the Masters if you think that you can get something *very* noteworthy from it (like an academic paper if you are able to do research with a professor). But if the likelihood of doing that is low, then you may as well just apply to a wide range of programs in the U.S. You could still potentially get into a top 30 program, but it's hard to say with all the competition from international Asian applicants. I would suggest talking to your professors about the possibility of research experience and trying to get more concrete data about alumni from your department who enrolled in Statistics PhD programs in the U.S. (i.e. where did they matriculate? Did they all have Masters degrees before matriculating? etc.).

It shouldn't be a problem. There are a lot of Statistics PhD students who go back to get their PhD after years of working in industry. I can think of a few Stat PhD alumni from UC Berkeley who *began* their PhD 9-11 years AFTER they finished their Bachelor's degree (and so they finished their PhD 14+ years after graduating undergrad). If you have been out of school for awhile, you should mainly convey in your application why you are interested in obtaining a PhD and why you are motivated to do PhD research, so adcoms do not perceive you as a "flight risk." If you have been working in industry for awhile, I don't think schools will expect you to have been doing much academic research. In general, the best letters of recommendation are those that can speak to your "research potential" and your mathematical aptitude, which is why letters of recommendation from academics are in general preferable. I'm not sure how much weight a letter of recommendation from a boss/supervisor in a non-academic setting would carry unless it speaks specifically to your math abilities or your research 'potential' -- though I could see there being a few exceptions (e.g. if you've been working in a national lab or a research analyst at an economic organization or something like that). I think your undergrad and grad performance are strong enough that you do not need to spend extra time working on your research portfolio. I would just apply for the PhD programs. Try to get some solid letters of recommendation, and I think you will see pretty good results at programs in the top 20... especially if you attended a top 3 program for both undergrad and grad!

If you apply to more Statistics programs, I could see you getting into some programs at larger state schools like Iowa State, Purdue, and Texas A&M... it will probably depend on the strength of your recommendation letters. I would add those to your list of "target" schools, as well as a few "safe" schools ranked in the 35-50 range.

1. Do you think a B on Real Analysis will have a critical effect on my admission? Hard to tell. Your overall GPA and grades look strong, but there are other very strong applicants from South Korea. It also seems as though the main issue is that other top-tier applicants from South Korea have many more math classes and research experience in statistics than you do. Your research experience in the Sociology Lab may be viewed as a positive, but it wouldn't be on par with a statistics paper. 2. Do you think intern experience at an IT company can have any effect on the admission? It probably won't have much effect on admissions. 3. Will having grad-level courses in advance positively affect my admission? Yes, that definitely would help. 4. Do you think getting admission to top-30-ish programs (up to Yale I guess? based on US News) seem possible with this profile? It seems like admissions for Korean students is very competitive. I personally know Koreans who had publications in respectable journals like Computational Statistics and Data Analysis, Bayesian Analysis, Statistics (some with multiple publications in such venues) who were "only" able to get into Texas A&M and North Carolina State University, but not Ivy League schools, Stanford, or CMU. In your case, I would target mainly large PhD programs at large public universities. It would be prudent to apply to lower-ranked programs too. I think you might stand a good chance at schools ranked around 40, e.g. UIUC, University of Florida, Rutgers, Ohio State, etc.

Your profile looks pretty strong. I think you should apply mainly to top 30 PhD programs, with a few "safer" options for good measure.

There are some outstanding theoreticians who graduated from Johns Hopkins Biostat and Harvard Biostat. Some I listed above, but there are others as well. I think it probably depends on who you work with as your PhD advisor at these schools.