biostat_prof

Once again it's hard to compare the two programs without knowing exactly which classes you would be taking and exactly what type of funding (if any) you would receive at either school. In any event, I can definitely tell you that programming will almost certainly be more useful than abstract algebra or PDE. I've never heard of anyone using abstract algebra or PDE in a statistics PhD, whereas programming skills can be very useful for some types of applied statistics. Granted, the areas where you would need to do C/Fortran programming in statistics are fairly narrow, so I wouldn't make these classes a priority, but I would definitely take more programming rather than abstract algebra or PDE.

Look at how much the professor has published and where they published. If they have lots of papers in JASA/Annals/JRSSB/etc., that is a good sign. You can also just ask your current professors what they know about the reputations of various programs. If you have any specific questions, I'll do my best to answer them.

That's hard to say. If you got bad grades in first-semester calculus many years ago and good grades in more advanced courses more recently, I doubt it would hurt you that much. (It might be worthwhile to explain what happened in your personal statement, though.) Having said that, if the bad grades were in a really critical class like analysis or linear algebra, I would give some serious thought to retaking the class (if you haven't done that already). There simply isn't much margin of error for international applicants these days, so a bad grade in a core class might very well doom you, particularly at the most competitive schools. Again, this is something that varies greatly from department to department (and from admissions committee member to admissions committee member). In general, I think what you say is correct: A very low score will raise some eyebrows, and an 800 will bump you up a bit, but generally it's used as a "weeder" if it's used at all. (Basically, they use low scores to filter out uncompetitive applications, and that's about it.) But it really varies. I talked to one person on our admissions committee who said that they give higher priority to people with 800 scores. Another person said that they don't consider it at all unless the score is very low. And anecdotally there is an undergraduate at my school who had a sub-750 math GRE score and they still have been accepted to virtually every program to which they applied. (Although this person had a nearly perfect GPA and incredible recommendations/research experience, and they still got rejected at some of the top-ranked schools.) If you have some questionable math grades, I do think it would be worthwhile to study until you're confident that you will get a nearly perfect score on that exam.

Yeah, I think the statistics MS is the better option of the two choices listed above. Complex analysis almost certainly won't help you, and numerical analysis/linear optimization probably won't be useful unless you decide to pursue a few very narrow specialties. Real analysis and mathematical statistics will be useful, but it sounds like you can get that from the stat MS anyway. And the stat MS will also give you exposure to some core stat courses that you will probably encounter in grad school. Having said that, real analysis would be a far better choice for your elective courses in the statistics program than a class in data mining/time series analysis/sampling. Honestly, unless you are applying to a department that is strong in one of those areas, none of those courses will probably help you that much, but almost every school will expect you to have taken real analysis. If you can get some exposure to measure theory as well, that would also be useful. (You will often get this as part of an advanced probability course.) As I said earlier, there is a common mindset that anyone can learn to write SAS code to do time series analysis, but not everyone is smart enough to do complex theoretical math, so analysis/measure theory would be better electives than other applied statistics courses. I hope that helps. Let me know if you have any more questions.

I'm still a little unclear about which courses you would take for which degree. And are you considering just taking some additional courses without getting an MS as well? If real analysis is the only relevant math course in the math MS program, then maybe a stat MS is a better option, assuming that you can still take real analysis. Although if you can take any courses in measure theory/mathematical statistics/measure-theoretic probability, that will help you as well. I wouldn't bother with complex analysis, and stochastic processes is questionable. If you list exactly which courses you would be taking for the two degree programs, I can try to give you better advice.

It's very difficult to give a general answer to this question, because every admissions committee is going to look for something slightly different. Having said that, in general, you are probably better off taking more upper-division math courses. The biggest concern of most admissions committees is that you won't be able to pass your qualifying exams if you can't do the math. Also, there is a mindset (incorrect in my opinion) that you can teach a person to do applied statistics or computational work but mathematical ability is something innate, and if you're not a strong "math person" than you'll never be able to do high-level theoretical work. Also, nearly every statistics graduate program will require you to do some sort of coursework in theoretical statistics and probability, but the applied curriculum varies greatly from school to school. Thus, if you take more applied statistics courses, you may have a bunch of applied courses on your transcript in areas that you will end up never using in grad school. (Well, pretty much every program will require a course in linear models, but otherwise there is a lot of variation.) So in general, I would definitely error on the side of more math (or theoretical statistics/probability). Analysis, measure theory, theoretical statistics, and measure-theoretic probability would all be fantastic, as would advanced linear algebra. You probably won't get much mileage from complex analysis or topology, though. Also, if you are interested in applied statistics, you should look into the possibility of taking some computer science courses. Demonstrating programming ability will probably help you more than applied statistics courses for the reasons I listed above. As for research, I would say that it is far more important to find a project where you can make a major contribution and get a strong recommendation than it is to find a project in a specified area. You would be better off working in an area that is only tangentially related to statistics if it will result in a first-author paper and a superb recommendation than a project where you're basically just making photocopies for a superstar statistician, if that makes sense. The main thing is to demonstrate that you can do independent research, so anything you can do to provide evidence of that would be a good idea. Let me know if you have any other questions. I don't check this board frequently, but I'll try to answer your questions when I come on here.

<br /><br /><br /> Yeah, not having taking analysis might have hurt you as well... (If the proof-based calculus class that you took didn't have a title like "analysis" or "advanced calculus," they might have assumed that you haven't had enough exposure to proofs and stuff like that. Most people we admit have had a year or more of analysis.) But I think your international status was what really did you in. As I said, I'm at a top-ranked biostat department and I know that we have admitted people with much less impressive credentials than that, but we only have funding for 1-2 international students this year most likely. Good luck in your neuroscience program; I'm glad things worked out for you.

Yikes! For what it's worth, I can tell you right now that the fact that you are not a U.S. citizen was what killed you. My department is very highly ranked, and we have admitted a number of students with credentials far inferior to yours. But this year was absurdly competitive. We had a 50% increase in PhD applications compared to last year (which was already higher than normal) and very little funding, so admissions was a bloodbath. I think we only admitted 1-2 foreign students, since they are harder to fund for various reasons. If you were a U.S. citizen, you almost certainly would have been accepted somewhere. And with those stats, I'm still surprised that you didn't get at least one stat PhD program to accept you. Did you only apply to Stanford and Berkeley or something? Sheesh. It's probably too late now, but I'm tempted to try to convince you to apply to my department and come work for me. I would definitely love a student with those credentials. The only other possible issue I can think of is your mathematical background. Usually we are more concerned with a person's grades in their advanced math courses than in their stat courses. If you haven't had all the mathematical prerequisites, that might have scared some schools away. Still, I would have thought that the rest of your application would have more than compensated for that. (And I would like to think that the requisite math courses would have been part of your degree if you were a stat major.) Sorry for the bad luck. I guess being accepted into your first-choice field is a decent consolation prize. Good luck with whatever you decide to do.

I am a professor at a top-ten biostat department. For the most part, the USNWR rankings of stat/biostat programs are fairly close to reality. Having said that, they are often misused by students, since in a PhD program, the reputation of one's dissertation adviser is more important than the reputation of one's school. You would be better served to attend a lower-ranked department with several strong faculty in your area of interest than a higher-ranked department where there are no faculty for you to work with. Also, note that the rankings for schools 9-27 or so are probably within the margin of error of one another. This is another reason to consider the faculty that you might work with rather than the ranking of a school. Of the schools that you listed, Harvard is outstanding, although admissions will probably be very competitive. My impression is that Yale has several strong younger faculty, although they have fewer superstar senior faculty. I'm not very familiar with the department at BU. As for Brown, that is a very new department, which is probably why it is unranked. I have no idea who they have hired, although attending a new department like that is always a bit of a gamble. Was there some particular reason you only applied to schools in the New England area? One way or another, it is probably a moot point, since you have probably missed the deadlines for most (if not all) biostat PhD programs by now. If you're serious about this, you may want to apply again next year. The admissions process is brutally competitive this year, and you really should apply to more than four schools unless you have superstar credentials. (And if you're not a U.S. citizen, it's going to be even uglier. I would recommend that non-citizens to at least 15-20 schools unless your resume is absolutely flawless.)