sisyphus1

are you applying for master's or phd programs? for master's programs i think your math background is more than adequate - you will be competitive at most top programs (in my master's class there were many students with far less mathematical background than you).

i would advise (2).

hey - also interested in machine learning/data mining (they mean the same thing) here. some good programs would be: stanford berkeley CMU duke michigan most stat departments will have at least one or two professors working on machine learning these days, given that it's such a 'hot' area. having said that, try to get some machine learning related research under your belt before you apply to these programs.

hmm i think you have a good range of schools - a bit top heavy though. even Columbia, ranked outside of top 20, has an insanely low admit rate (columbia stats MS here). . i would pick a couple of schools outside of top 25 as your safeties If you want to do applied work UChicago might not be the best. also while they don't require the math GRE they highly recommend it. maybe replace Uchicago with CMU if you are interested in applied work (esp. machine learning).

you could spend a part of your SoP delving into a research interest (in your case, database systems). but at the end you could have a paragraph saying that database systems is your current interest, and that you maintain broad interests in other areas (this is what im doing for my SoP).

Here's my profile: Undergrad: 3.9 (math/econ from upenn) Grad: 3.9 (stat master's from columbia) GRE (Q/V/W): 800/590/5.0 LoR/SoP: nothing outstanding, but not bad either Schools (for biostats phd): reach: harvard uwashington johns hopkins match: yale ucla columbia International applicant from europe. I'm also applying to a handful of stat phds

Agreed - I think you should sprinkle the math PhD applications with a few stats programs given your interest in probability. You will be competitive for all top 10 stats programs IMO. Don't really know much about math PhDs but again, I would be very surprised if you didn't get into a top 15 program. You have a great application (certainly better than mine!) so don't put yourself down! Best of luck

i would also note that many undergrad "math" majors (esp from US universities) haven't had many more math courses than the OP. math requirement at my school (it's a decent school) was calculus I-III, linear algebra, ODE, real analysis, number theory, upper level linear algebra, and a couple of electives that could easily be filled by "softer" econ/finance/comp sci courses (econometrics, financial engineering etc. this is the route a lot of students, including me, took). of course, there were a few hardcore math students who took topology, abstract algebra, honors-level real analysis etc., but from my observations they were definitely the minority

Reference from professors would obviously carry more weight, but between an outstanding recommendation from employer versus a lukewarm recommendation from a professor, I would choose the former. I think for Master's programs they are more lenient with regard to accepting recommendations from employers. FYI, I didn't develop any relationships with professors during my undergrad, so I had to rely mainly on employer recs when I was applying to Master's programs. I had 1 prof recommendation and 2 employer recommendations, and got into Columbia stats for masters. Given that you work at a pharma company, I am guessing most of your managers will have PhD's in Stats/Biostats/Sciences? I think they will be fine. Bottom line - no need to worry about it too much. Best of luck in the process

the only 'slight' red flags might be your GPA and the perceived rigor of your undergrad institution, but i think you have a good shot at all the top programs. having said that, i would narrow your list down to 3 reaches, 3 matches, and a couple of safeties.

I'm in the process of gearing up for the upcoming application season, and I've been doing some research on the different biostat programs. From looking at various departments and dissertation topics of recent grads, it seems like there are two broad subfields within biostats: 1. 'traditional' biostats (focusing on application of statistics in more traditional fields like clinical studies, epidemiology, public health) 2. 'computational' biology (application of statistics in genetics, neuroscience, biomedical sciences etc.) Most biostat programs seemed to focus on 1 (yale, columbia, upenn, and most other schools whose programs reside in the school of public health), while there are a handful that have a focus on both (harvard, johns hopkins, university of washington, from some cursory research). I can honestly say that I have a genuine interest in both - while 1 seems less 'sexy', I can see myself being (for example) a statistician for an NGO/Government/UN, using statistics to guide public health policies. For 2, I've taken a few courses in machine learning where I was exposed to examples of machine learning techniques used in the context of biology, and I found them absolutely fascinating. Another way to look at it would be that from a career perspective, 1 seems more relevant, while from an intellectual curiosity/research standpoint, 2 is more appealing. Given, the above, I was wondering if people could advise on the following: - is my broad classification correct? or too myopic? - given that 1 and 2 are quite different, should I mention that I have an interest in both (in my personal statement) or pick one and go with it? - if I had to stick to a field, it would probably be 2. however, 2 seems like you would need some exposure to biology, and I have none. - aside from the schools mentioned above, which other biostatistics departments have a good deal of professors working on computational biology-type problems? Thanks all and good luck to everyone in the upcoming application process.

It depends if you have had previous finance experience through coursework and/or internships (preferably both). From looking at your posts, it seems like you do not. Further, it seems like you don't really know what being a 'quant' entails. I suggest you: - take some financial engineering classes to get a flavor for it - take some grad level econ classes. and take it from there (i.e. whether you want to be a 'quant' or apply to Econ PhD). At the risk of sounding harsh, let me warn you - Econ PhD's are insanely, insanely competitive. Quant jobs are even more competitive. Best of luck in the process.

finance/business is such a broad area and my advice will depend on what you want to do: if you are interested in 'quant' side of finance/business (financial engineering, risk management, statistical consulting), i would suggest you stay in your PhD program until you get your Master's at least. However, make sure the courses you take are going to be relevant to the field that you are interested in, and make sure your summer internships are relevant (e.g. work at an investment bank doing market risk versus being a research assistant). A lot of corporations have targeted internship programs for PhD students so look into that. if you are interested in more 'business' side of finance (ibanking, consulting etc.), i suggest you drop out, but not before you have a plan of action. an MBA will help but it will heavily depend on where you go. how long is your work experience? decent MBA programs will expect at least 2 years of work experience, while the average work experience will be 4~6 years. With your nonprofit experience and the MA from columbia you could have a shot at few of the top programs.

im sure cyberwulf can give you better guidance, but from what i see i think your are being too humble. yes, gpa is on the lower side but seems like you've taken some hard, grad-level coursese which should help counteract the low gpa. combine that with research experience and letters of recs, i think you will be competitive at almost all biostats programs that you apply to (keep in mind that biostats phds are not as competitive as stats phds).

i know the top schools in machine learning are CMU, Cornell, Stanford, MIT, but what are some other schools who have decent machine learning departments (who i believe are either in stats or computer science departments atmost unis)? i've gotten a serious interest in ML but i have virtually no shot at the top programs, so im trying to gauge what the next 'tier' schools are.

are you aiming for phd or master's program? multivariable calc, linear algebra, and perhaps a calc-based probability course should be sufficient for master's programs.

agreed. your stats are as good as it comes. congrats on your achievements so far and good luck

Yep, I wanted to see what kind of (real world) problems biostaticians work on. I'll definitely check taht book out. Thanks (as always)!

can anyone recommend a good book (textbook or otherwise) on introducing oneself to biostatistics? what's the standard textbook for 1st year biostats students? I understand that it's quite a large field (from epidemic analysis to pharmaceutical testing), but i want to read up on some topics to make sure I am understanding what I'm getting myself into.

I am interested in applying to stats/biostats phd programs for the upcoming semester, and I had question about supplemening your application with a research paper. For one of my classes, we had to do a semester-long group project on a topic, and while I do not believe it to be publication worthy, I worked extremely hard on it (100+ hours) and achieved some interesting results. Is this worth sending in along with my application? I am afraid that it may not be phd-level worthy and the admissions committee might see it as too 'juvenile'... Has anyone had experience in sending such supplemental material?

cornell MPS will be a better fit from an applied perspective

What's your background? If you have no experience finance, definitely take an intro to quant finance course (make sure they are using the Hull book). Also, what do you want to within finance? This industry (especially quant finance) is experiencing massive saturation and many candidates from top Masters MFE/IEOR/STATS programs are having a hard time finding jobs. lastly, i would recommend you check out www.quantnet.com to get some quality info

thanks all! i was thinking of my statement of purpose, and i heard it may be disadvantageous to say that you want to pursue a non-research/academic career. some programs (e.g. wharton) explicitly state that they want to train academic researchers on their website. any truth to this?

All, I've decided to apply to PhD Statistics/Biostatistics programs for next year (2013). I'm currently employed as a quantitative finance analyst at a major bank but I am looking to leave the finance industry. I am interested in using statistics to guide public policy/public health type decisions (hence the interest in Biostats), as well as statistical consulting. I do not see myself in academia so the 'brand name' matters somewhat in getting companies to interview you coming out of the PhD program. Profile Undergrad GPA: 3.9, Math/Econ from Upenn. But my coursework wasn't that rigorous in that I took the easiest math classes to graduate (also I got a low grade in Real Analysis... I was lazy and didn't do any of the homework). Not many stats courses other than an Econometrics course. Graduate (Masters in Statistics): 3.9 from Columbia. Took a couple of PhD level courses. Letters of recommendation: They will be pretty good, but not from well-known professors. GRE: 800Q, 590V, 5AW Research experience: None (I think this will be a major flag... For one of my Master's classes we had to do a project on a topic (our group's topic was on using a refined metropolis-hastings algorithm to solve a text-mining problem). I thought it was really cool but probably not publication-worthy). Demographic: International student from Europe Other: Proficient in R, SAS, MATLAB, SQL etc. Will have 4 years of work experience when I matriculate (god I feel old). My initial selection of schools is as follows: Yale, Duke, Harvard (biostats), UCLA, Also looking at IEOR PhDs - how competitive are these compared to PhDs in stats? I feel like I am aiming too high... can you provide feedback? Thanks!

Agreed with above posters - UMICH will most likely provide better opportunities. Congratulations on both offers! I am aiming to apply to both programs next year - any chance you can post your profile (without being too specific of course)? GRE range, undergrad gpa, undergrad major, previous research experience etc...