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health_quant

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  1. Upvote
    health_quant got a reaction from Statboy in Stats program by tiers?   
    haha. so true. spending all our time on thegradcafe doesn't help with maintaining a broader perspective...
  2. Downvote
    health_quant got a reaction from MLHopeful in Stats program by tiers?   
    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.
  3. Upvote
    health_quant reacted to wine in coffee cups in Should be an easy decision, but...   
    Oh, I have truly no idea about that, sorry man. I know the UCSD math department is overall solid, so I would guess the prob/stat group alumni fare pretty well, but you'll have to investigate that yourself if you don't know already.
     
    I think the issue is more if you feel that your interests can still be served by the department even if they shift around a lot, which they basically do for everyone. Also, you are sooo young and have blitzed through undergrad in two years!! I would be wary of a program that limits your intellectual options and perspective because you haven't really had the opportunity yet to explore or learn what it feels like to hit a wall/ceiling. My interests are still solidifying, but the general areas I've more or less settled on were picked up at various points over the six years from my senior year of college to the present day. My education before that certainly helped develop my mathematical skills but was not informative as to what I wanted to study (even though at age 20 I would have disagreed and told you that I'd do a pure math PhD right out of college, lololol, because that's all I really knew at the time). I personally needed those extra years to encounter some limitations, get sick of where I was heading, try out new things, read a lot, start caring about areas of application I had no interest in before, etc. And now I'm glad to be in a place that serves my current set of interests well but will continue to work even with some evolution.
     
    Statistics is a broad field, so I think if you feel awesome about it now you're not likely to have a complete about-face, but I wouldn't count on more intellectual stability than that. Maybe UCSD can accommodate a wider range of interests than I am uninformedly assuming, maybe not, but I think you should mull on that. I also think you'll learn a little more about the possibilities open to you and which doors you feel okay closing at this early stage in your career by way of comparison with UNC this week.
  4. Upvote
    health_quant got a reaction from clamofee in Top 2 Biostat vs. Top 10 Stat PhD   
    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. 
  5. Upvote
    health_quant reacted to student12345 in PhD Stats:How to better position myself for next season?   
    Don't worry about getting an RA or whatever. Stats/Math work isn't done in labs. You can start collaborating with people at your undergraduate institution. Definitely contact the professors that had the best impression of you and ask them if there's anything that you could help them with!
  6. Upvote
    health_quant reacted to cyberwulf in Graduates in Biostatistics   
    I agree with much of what biostat_prof laid out above. A few points of difference (and some of agreement):
     
    - At the risk prolonging a fruitless debate, I don't think one can argue that there is at least a general perception among students and many faculty that UW/Hopkins/Harvard are a notch above other departments. Obviously, there are some metrics which would disagree with this ranking and others which would agree, but one result of this "perception gap" is that these three schools attract the strongest applicant pools and have the strongest (on average) incoming classes. The fact that the strongest students are most likely to end up in top academic positions serves to reinforce the perception gap, though these students might have been equally successful at other schools. The obvious corollary to this is that a strong student need not attend a "top 3" school to be successful.
     
    - biostat_prof is exactly right that the productivity and reputation of your adviser matters much more than the name on the front gates of campus; the advantage of a higher-ranked school, then, is that increases the chances that you will find a productive, well-known adviser to work with. Things rarely go exactly as planned in graduate school, so being at a top place provides some comfort in knowing that if you "fall into" (or get assigned to) a project with almost any faculty member, turning that project into a dissertation will set you up well to achieve your goals, whether they be a job in industry or a tenure-track faculty position. As you go down the rankings, a student has to be more mindful of seeking out an appropriate adviser to match what they want to achieve.
     
    - The industry job market seems pretty strong to me for both MS and PhD grads, and might even be stronger for the latter than the former. PhD graduates from our department looking to work in industry typically have jobs lined up before they graduate or shortly after; many looking down this path do summer internships at their eventual employers.
     
    - Academic positions are a different story. A strong publication record (1-2 first authored in good journals minimum) has become a de facto lower bound for hiring at methods-oriented departments (roughly the top 10-12), and more students are taking post-doctoral positions to boost their CV even beyond this. I have to admit I'm not as familiar with hiring at lower-level departments, but I think that most good graduates of the top handful of departments would be able to find a position at a lower-ranked place. Some of the perceived 'tightening' of the academic job market may be a result of graduates of these top departments having unrealistic expectations about where they might be hired, and hence being 'too proud' to apply to places down the ladder, or to research-track appointments. 
  7. Upvote
    health_quant reacted to are_we_there_yet? in Who's sitting on no acceptances? Come commiserate   
    For Biostat: 1 rejection, three MS with no funding (yet), and 5 waiting. Unfortunately four of the waiting are Vanderbilt, Yale, Brown, and Penn... which really doesn't bode well for me.
     
    For Stats: Waiting on Michigan State, but I anticipate a no-funding MS-PhD offer.
     
    At the urging of my research advisor, I sent out a late app to his alma matter for an applied mathematics PhD. I've done some research with the a member of the adcom, so I would like to think I at least have a shot. The program is stats heavy and I would be quite happy there.
     
    Chin up, everyone. While I, too, am freaking out, the music is still playing, there are plenty of seats left. It isn't even March yet.
  8. Upvote
    health_quant got a reaction from mmajum01 in Brown Biostatistics PhD: Multiple Rounds of Reviews   
    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.
  9. Upvote
    health_quant reacted to echlori in Whose heart was broken on Valentine's day?   
    Many other things
     
  10. Upvote
    health_quant reacted to wine in coffee cups in Conditional Probability of Acceptances   
    Are you not aspiring statisticians who need to redirect anxious energy?
     
    Scrape database results for stats/biostats applicants from past years. Parse out GPA/GRE/subject/citizenship when provided. Match results on those keys to track outcomes for individuals over the entire application season (maybe with manual cleanup to account for typos/transpositions). Use more complete posted profiles here and on mathematicsgre.com to augment and correct results.
     
    Then you have some data to actually estimate the conditional probabilities. Non-random sample and has errors, but better than nothing. You'll learn valuable skills on the way there.
  11. Upvote
    health_quant got a reaction from Penelope Higgins in Difficulty of First Year Courses   
    If you're serious about learning what's going on under the hood and you've had mathematics through multivariable calc, pick up Statistical Inference by Casella and Berger. This will take you through probability theory and mathematical statistics at the upper undergrad or lower grad level. However, if you're not planning on doing a lot of heavily quantitative research and/or teaching yourself a lot of advanced methods in the future, C&B (and what follows) would likely be overkill.
     
    Alternatives to C&B:
    Mathematical Statistics (Wackerly, Mendenhall, Scheaffer) - similar topics to Casella & Berger, but at a lower mathematical level, in my opinion Mathematical Statistics (Rice) - a lower mathematical level than WMS, but some with weaker math backgrounds may find it as a good intro to the topics Mathematical Statistics (Bickel, Doksum) - slightly higher level than C&B; this one does a better job of emphasizing estimation of multiple parameters, while C&B sticks more to single-parameter estimation  
    You will need a good background in regression to make use of all the above statistical theory. For that, you might try the following:
    Introductory Econometrics (Wooldridge) - this is used for the first-year quant methods courses in many soc programs (e.g., Penn's and UNC's); his upper-level book, Econometrics, is commonly used as an alternative graduate text on econometrics to Greene's Introduction to Linear Regression Analysis (Mongomery, Peck, Vining) - a good alternative to the introductory text by Wooldridge, and written more from the statisticians' perspective; note that you should have a background in multivariable calculus and linear algebra to get the most out of this book Linear Models in Statistics (Rencher, Schaalje) - good for self-study, as all solutions are provided in the back; the first few chapters review the essential linear algebra, but again, you should have already be familiar with eigenvalues, eigenvectors, spectral decomposition, etc. A first-year sequence in quant methods for soc will likely cover the basics of ANOVA, standard linear regression, logistic regression, and Poisson regression (all three of which are encompassed by generalized linear models) and possibly touch on multilevel and longitudinal structures, survival analysis, and causal inference. With stats, you can go much, much deeper than what I've listed above. That being said, I don't think it's necessary to know all of the above to be a good quantitative researcher in the social sciences, provided that you do have a solid understanding of the assumptions and limitations of whatever techniques you use.  
     
    As for taking the first-year graduate econometrics sequence: I wouldn't recommend doing this unless you have an extremely strong background in mathematics. Many of the top programs assume knowledge of real analysis and some familiarity with mathematical statistics before entering the program. Some top-10 programs' econometrics sequences even begin with an overview of measure-theoretic probability, and dive right into asymptotic properties of estimators. These are not trivial topics. FWIW, my own background is in math (undergrad) and biostats (grad).
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