health_quant
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2013 Fall
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biostats
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Statboy reacted to a post in a topic: Stats program by tiers?
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MLHopeful reacted to a post in a topic: Stats program by tiers?
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If you're interested in enrolling in a doctoral program after the MS, one big benefit of attending Brown's program is its small size. With fewer than 10 students entering each year (~3 being doctoral students), it may be easier for you to establish closer relationships with the faculty there. Letters of recommendation will be extremely important for further graduate study, and solid references may help to obtain a position in industry (though I don't know how common it is for employers to check recommendations for MS-level positions).
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health_quant reacted to a post in a topic: Should be an easy decision, but...
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clamofee reacted to a post in a topic: Top 2 Biostat vs. Top 10 Stat PhD
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Top 2 Biostat vs. Top 10 Stat PhD
health_quant replied to statprospect's topic in Mathematics and Statistics
I think you and I are in similar boats this year, and maybe even the exact same boat. (I am assuming that by second-tier stats program, you are still referring to a top 10 or top 15 stats program, as tiers on gradcafe seem to be used to distinguish between programs within that upper echelon.) I think that what earlier posters have noted about academic job placements (i.e., publications outweighing school names) really is the most important factor to consider if you want to go the academic route yourself down the road. The top biostats programs seem to do consistently well in preparing their students in this way (though biostat_prof has noted a general tightening in the job market), and if the stats program has had good placement recently, then I assume they've been supporting students well in this respect, too. Of course, any success has a lot to do with the students themselves and their relationships with advisers. Regarding private sector employment, I imagine that the relative importance of name and program-type will depend on the industry. If it's a highly statistical position, then I imagine that your hiring will be done by other statisticians, or at least people with an awareness of the general strength of stats/biostats programs. In this case, I feel like a candidate who would be competitive for an academic position would also be quite competitive for the private sector. I suppose if the private sector job really emphasizes applied, collaborative research, then a strong history of that may be quite helpful. April 15 is loooooming. I hope you're having an easier time deciding than I am. -
health_quant reacted to a post in a topic: PhD Stats:How to better position myself for next season?
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I'll echo Kimolas and recommend getting more research experience (and checking with your undergrad professors is a great idea). Your math background is far from weak, so I don't think that taking the math GRE will give admissions committees much more information on you as an applicant. For what it's worth, a friend of mine was admitted to Washington's PhD program in statistics without an undergrad major in math or stats. He did, however, have a lot of relevant research experience.
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health_quant reacted to a post in a topic: Graduates in Biostatistics
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Yale (Stats) vs University of Washington (Biostats)
health_quant replied to DMX's topic in Mathematics and Statistics
I would agree with you regarding Stanford and CMU, but UW versus Cornell, Michigan, or Wharton is definitely debatable (especially the latter). Among the new and/or incoming ML-related hires at UW: Carlos Guestrin (formerly a prof at CMU in ML/CS); Emily Fox (formerly a prof at Wharton); Ben Taskar (CS/Stats at Penn Engineering/Wharton). There's definitely a big push from UW to build up ML in the stats department (while integrating with UW CSE), while biostats and stats have historically been linked quite closely. -
Does this mean i'm on the Waiting list?
health_quant replied to nooooob's topic in Mathematics and Statistics
Uromastyx is right on. For the school, extending an offer to someone who isn't completely committed to their program could tie up one of their admissions slots for weeks and delay them from extending the offer to someone on their waitlist. Just like we'd like to hear back from schools as soon as possible, schools want us to decide quickly, so that their own waitlist doesn't evaporate while those waitlisted students take other offers. If this is your top choice, let them know. It seems likely that they want to admit you, but they just want some assurance that they're not wasting their time. -
health_quant reacted to a post in a topic: Who's sitting on no acceptances? Come commiserate
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Brown Biostatistics PhD: Multiple Rounds of Reviews
health_quant replied to mmajum01's topic in Mathematics and Statistics
I don't know for certain, but if they're doing more rounds of reviews, then it seems their offers wouldn't be limited to people who attended that day. Given the strength of their program, I imagine that Brown's yield is good, but it's still possible that they could exhaust their initial list of interviewees without filling every slot, as those initial people are probably receiving multiple competitive offers. I think that at this point, we should consider no news to be good news (which also applies to any other schools from whom we haven't heard). -
mmajum01 reacted to a post in a topic: Brown Biostatistics PhD: Multiple Rounds of Reviews
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Brown Biostatistics PhD: Multiple Rounds of Reviews
health_quant replied to mmajum01's topic in Mathematics and Statistics
Brown's biostats program is very careful with its admissions process, as they only aim to admit 3 or so doctoral students per year. During the (first? only?) interview/recruitment weekend this year, there were only about 8 applicants present per arm of their public health program (biostats, epi, health services research). Despite being relatively small, the biostats department is doing some extremely interesting work. They're definitely among the top picks of the schools to which I applied. Fingers crossed that we all hear some good news soon. -
health_quant reacted to a post in a topic: Whose heart was broken on Valentine's day?
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As others have pointed out, all of the aforementioned are top-notch programs, but it seems that even within this upper echelon of schools, distinctions are still commonly drawn. When we're looking at the total population of schools, I agree that these are nearly all top-tier programs. As far as this thread goes, the distinctions seem to boil down to drawing sub-tiers within that topmost tier.
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UIUC Admissions Decisions Out
health_quant replied to OhioStateStudent's topic in Mathematics and Statistics
Congratulations! -
How long are statistics waitlists usually?
health_quant replied to tcbh's topic in Mathematics and Statistics
Same here. I'm scratching my head because I've gottten responses from Washington (biostats) and Wisconsin (stats), both with full funding, but not a peep from UNC. I thought the concordance of admissions from these places would be pretty high... -
Program Rankings vs Fit (Biostat vs. Stat)
health_quant replied to bayessays's topic in Mathematics and Statistics
The level of theory seems to vary quite a bit across Biostats departments. For Michigan, the Biostats doctoral students take the same requisite graduate, upper-division sequence in mathematical statistics as Stats doctoral students, but are not required to take the measure-theoretic course in probability. Perhaps it's also worth noting that Michigan's more measure-theoretic coursework is cross-listed in the Math department. Similarly, Penn's measure-theoretic probability sequence (with stochastic processes) is cross-listed under the Math dept. The doctoral-level stats course in mathematical statistics is taken concurrently with measure-theoretic probability, so it seems unlikely that their mathematical stats work(at least at that level) requires measure theory. Penn's Biostats department doesn't require measure theory (as it has its own sequence in probability and mathematical statistics), but evidently, many of the theoretically-inclined Biostats students take the aforementioned probability sequence through the Stats/Math department as electives. The University of Washington's probability and mathematical stats courses are the same for the Stats and Biostats departments. From what I understand, students from both programs take the same theoretical sequence for the first two years. (I believe UWashington is known for being one of the more rigorous biostats programs.) Wisconsin-Madison's Biostats program is just a concentration within Stats. In this case, the probability and math stats coursework are the same, i.e., lots of measure theory. I would imagine that biostatisticians with strong theoretical backgrounds (from the stronger programs) might still be viable in stats departments, but given the number of Biostats departments across the country (and industry positions), I don't think there's much reason for them to pursue those jobs. When you flip through the top stats journals (e.g., JASA, JRSSB), you'll see an abundance of biostatisticians along with statisticians. For us, the distinction seems to arise more from our relative emphasis on biomedical research applications. Even with Stats departments, research in cutting-edge methodology (which of course requires extremely strong theoretical backgrounds) is gaining in importance. We can see that with how the University of Chicago's been updating its Stats department, and with the number of hires from other backgrounds (especially EECS) for machine/statistical learning, and topics in non-parametric Bayes. Really, it seems like much of the disciplinary boundaries between Biostats, Stats, and CS are blurring now more than ever. More traditional topics in theory, (e.g., probability and stochastic processes) still seem as much the domain of mathematicians and applied mathematicians as of statisticians. -
haha. so true. spending all our time on thegradcafe doesn't help with maintaining a broader perspective...
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Their placements in biostats departments are quite good (high numbers of graduates in well-ranked programs), but I know less about their placements in stats departments.
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Biostat: Minnesota ph.d vs Berkeley ?
health_quant replied to XXQQCC's topic in Mathematics and Statistics
This is definitely going to be a tough decision, so don't feel pressured into rushing it. Whether you decide to attend Berkeley or Minnesota, you'll be in a very good position down the road. Some points in favor of Minnesota (as my last post was kinda pro-Berkeley): As others have already pointed out, Minnesota is a top-notch program, and at least in biostats, comparable to (if not outright stronger than) Berkeley. If you're planning on going into a non-academic (or even non-statistical) career, the Berkeley name may count for more, but within biostats, people will know what Minnesota means. As others have also pointed out, there's no guarantee that you will be able to stay at Berkeley. Your dad has a good point in that another round of applications during your second year will be taxing, and certainly distracting from any academic work/research in which you might otherwise be engaged. Should you decide to move into a different program, you will likely have 3 years in your next school (if they have an accelerated program for MS students like Michigan and Minnesota). In this case, your connections with faculty there may become quite strong, but they may not be as strong as connections you might forge over a full 5 years in one program. (Of course, they could be just as strong...who knows, right?) Also, some programs may not accept transfer credits, in which case you're in for another 5 years at least. The plus-side to this is that you'll really know your stuff once you leave, but the con may be that this could be overkill for any non-academic posts. Just another 2 cents from someone who still hasn't chosen a program himself. -
Biostat: Minnesota ph.d vs Berkeley ?
health_quant replied to XXQQCC's topic in Mathematics and Statistics
I think Berkeley is still worth serious consideration. Being a funded MS student at a relatively small department should afford you a number of good research opportunities with well-respected faculty in biostats (and possibly stats). Assuming that you make a good impression there, you would be extremely competitive for doctoral programs at the top schools (Hopkins, Harvard, UW Seattle), and you would presumably still be at least as competitive for Minnesota then as you are now. I may be biased though, as I'm currently a funded MS student (at a comparable biostats program to Berkeley's). For what it's worth, I was accepted at the University of Washington this cycle, which likely wouldn't have happened without the research experience and recommendations from the MS.