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Take linear algebra and multivariable calculus (sometimes called calculus III). Since you are leaning towards applied stats, a programming class couldn't hurt. Most reputable master's programs will have those two courses are requirements.

Ah ok. I had similar stats to you (also international) but had problems cracking the top 15. Best of luck!

which schools did you apply to last year? I am flabbergasted that you didn't get into any PhD programs--your profile is excellent.

From anecdotal evidence from Faculty/current PhD students, it seems like fewer and fewer stat programs require it (as part of their PhD curriculum). I would guess that this is in part due to Stats becoming more computational in nature.

I posted a similar thread on the Engineering forums, but I thought I might post here as well to get a different perspective. My interest is in machine learning. For some schools, this (mainly) falls under IEOR (e.g. Princeton). For some schools, this (mainly) falls under CS (e.g. NYU, CMU). And at other schools, this (mainly) falls under Stats (e.g. Stanford). I have no real preference for which department, as long as I can work with faculty doing ML. But I was interested in people's opinions as to which departments were (generally) more competitive? From my perusal of the forums/departments it seems like the competitiveness of admission (in order of decreasing competitiveness) is: CS (it seems like you can't even think about admission to the top 5 without publications) IEOR Stats. Anyone care to chime in?

My interest is in machine learning. For some schools, this (mainly) falls under IEOR (e.g. Princeton). For some schools, this (mainly) falls under CS (e.g. NYU, CMU). And at other schools, this (mainly) falls under Stats (e.g. Stanford). I have no real preference for which department, as long as I can work with faculty doing ML. But I was interested in people's opinions as to which departments were (generally) more competitive? From my perusal of the forums/departments it seems like the competitiveness of admission (in order of decreasing competitiveness) is: CS (it seems like you can't even think about admission to the top 5 without publications) IEOR Stats. Anyone care to chime in?

Is this conditioned on domestic applicants? My experience has been more in line with what wine-in-coffee-cups has said. Most of my math-major friends (who realized early on that they didn't want to go for a math PhD) had 3.9+, because: 1) they took only the pre-requisites 2) math classes had a very generous curve And for the semi-advanced math courses (upper level linear algebra, intro number theory, intro topology etc.), the classes were usually curved to B+/A-. This was at a private institution--perhaps there is not as much grade inflation at public schools.

Interesting... I think being an international certainly hurt me somewhat, and my letters were your run-of-the-mill "did well in class" type letters. I was able to talk to a couple of people from programs from which I was rejected, and from their description it seemed like the bar was far higher than I had initially thought (especially at Stats programs).

I think comleting an MS is a sound strategy (hopefully you have some money saved up from your employment? If you're in NYC there are decent part-time options at Columbia and NYU--hit me up if you're interested). Also, think about what your end goals are. If you want to do develop new statistical methodologies and stay in academia, PhD is probably right. If you want to be a practioner and work in industry (which is the sense I get from your short statement), an MS may be just fine. You can use the MS degree to see where you lie on this spectrum of applied vs theory--take some high level theory classes (i.e. beyond your usual probability/inference classes), and also some practical classes (most MS programs will have a 'Data Analysis' type course. Take them seriously).

I think the above poster is a little bit too optimistic. I had a similar profile to you (similar major, similar GPA, slightly stronger school, worked at a bank for 3 years, had a Stats Master's too) but failed to crack the top 15 PhD stats programs. I think you have a small shot at UNC and (maybe) Michigan PhD programs. For Master's though, I think you will be competitive at virtually all stats programs.

Depends on your research interest, but I would say (in order of decreasing importance): R, MATLAB, and C++. Emphasis on the last two if you are interested in developing algorithm

bootstrapping will work

Hi all, I was wondering if any areas within Biostats/Stats are considered especially "hot" these days? This shouldn't dictate one's research interest of course, but I feel like it should play a factor (with the caveat that a hot field now may be all but forgotten in a decade). From doing some cursory research it seems like: Biostats: - statistical genetics--with human sequencing becoming ever cheaper, I think statistical geneticists will be in great demand - computational neuroscience (especially with Obama's BRAIN initiative in the US)--how much room is there for biostatisticians in comp. neuroscience though? Seems like its more dominated by applied mathematicians and computer scientists Stats: - machine learning--no brainer. - empirical bayes--trying to estimate baysian hyperparameters from data is becoming ever more important with larger datasets... (or is it?) - artificial intelligence. Would appreciate any thoughts, especially from faculty/PhD students.

what are you looking to do with your financial engineering degree? if looking to go into industry, be mindful of the fact that the job market is highly highly saturated

Anybody know anything about this program? http://cds.nyu.edu/academics/ms-in-data-science/curriculum/ It's a new program, and from perusing the faculty list it seems like that have some really renowned people.

did you get funding? if not, the MS school will be understanding. but do let them know sooner rather than later

Anyone know anything about this program? http://cds.nyu.edu/academics/ms-in-data-science/ It's a new program with inaugural class for Fall 2013... upon seeing the faculty list it seems very legit (Yann Lecun for instance), but anyone else heard of it? Or know people who've applied to it?

berkeley and stanford are the more "choosier" of the MS programs, but i think you will have a solid shot at both. any reason why you are focusing on only those two?

I think a fully funded Princeton offer is hard to pass up... Stanford's ML is second to none but a lot of their top faculty are in stat departments (Hastie, Friedman etc.)

Hi all, How accommodating are schools in deferring funded PhD offers? I've searched the forums and it seems like deferring un-funded offers (e.g. for Master's) is no problem, but funded PhD offers are usually not defer-able. Also, should I let the schools know that I want to defer after accepting their offer? Or should I ask before April 15th? Thank you.

I wonder if any biostatistics graduates end up with faculty positions at non-biostatistics departments (stats/applied math/cs etc.)? I've heard some people comment that graduates of theoretical biostats programs (typically the top 3) have some success finding positions at stats departments.

What Shostakovich is true (at least from my observations). I work in quantitative finance and in our team there are several biostatistics PhDs.

phd program is quite strong. likelihood of continuing from MA to PhD is close to 0, unless you get some fantastic research and/or good number of phd-level classes under your belt.

depends on what you want from it. in general though, it's not a very reputed program (columbia stats MA here myself)

Hi, thank you for your comments. Not sure I agree--on what basis do you say that ML has historically been a strength of Yale? Chair of Yale stats department does work on ML according to the website, but if you look at his publications you see that it's not really ML.