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Everything posted by bayessays

  1. I would think most programs would automatically reject you because you don't have the calculus/linear algebra classes that are required.
  2. I'd check into what your dissertation would be like at CMU and how their graduates fair on the job market with a more interdisciplinary degree (although it seems like they get the same stats background as regular stats PhDs so it should be fine). Michigan has the Institute for Social Research and the survey methodology program, which are top in the country. I don't think you can go wrong.
  3. You should definitely apply to PhD programs. You might be able to get into some top 5 programs, definitely should be able to get into some top 10s.
  4. I do not recommend emailing professors. Your math grades aren't that bad coming from a good school. If your letters are decent, I imagine people will be impressed by your test scores and physics major with As from a top school. This isn't going to get you into Stanford, but I could see some top 20 programs accepting you. Go to US News rankings and start looking at programs in the 20-55 range which is more of a "match" range with lots of good programs.
  5. Given that you don't want to pursue an academic career and want to still be a data scientist afterwards, in my opinion you would just be taking a low paid 5 year (probably stressful) sabbatical from your job. If you already have a master's degree in statistics, the additional classes you would be taking would only be either highly theoretical math classes that you'll have no use for or electives. For the electives, you can just take free courses online in topics like NLP and deep learning that will be as good as any at a PhD program, and you'll have full control over your learning. After classes are done, you'll spend 3 years working alone on some very specific problem for a quarter of your current salary, and then you'll come out probably making less money than if you stayed at your current job 5 more years. I think getting the PhD only makes sense if you want to pursue academia, switch fields into something like biostatistics, or if your really have some deep internal need to have the experience of getting the PhD. You seem to be like being a data scientist and want to continue doing it, so I would take the money you're making now, attend some meetups or take some online classes, but i don't see it being worth the opportunity cost for you, which is probably something like $250-500k over 5 years. PhDs get the same boring data science job as people with master's degrees.
  6. This is incorrect and needs to stop being repeated. Some programs release decisions over a period of weeks for a variety of reasons. This is demoralizing to people whose applications haven't even been looked at yet and also gives false hope to people who are going to be rejected. Michigan probably has 100s of people they haven't sent rejections to yet and those people are not all "on a waitlist." I'm not sure if this is a language barrier, but a waitlist is a short list of people who may get acceptances depending on availability. The only wait list many applicants are on now is the waiting list to be sent a rejection email. We argued with you because you are wrong and claiming you know a process that you don't understand. You happened to get a waitlist from Michigan - lucky guess. Again, 100s of people did not get such an email, are waiting for their rejection letters and are not on a waitlist.
  7. Yeah it's more advanced than Casella and Berger but a more in-depth coverage of a lot of the same material (UMVUE, sufficiency, etc). It's hard to tell without looking at the homework, but stat 732 is pretty similar to what a lot of people would probably take as their their first post-CB theory course, followed by a course in large sample theory. Some top programs just skip having a CB course and learn the stuff at a higher level to begin with. I think a lot of programs are changing their curriculum and view spending multiple semesters on classical theory as irrelevant to most statisticians, and are changing the topics covered in advanced theory courses more to things like bootstrap, empirical processes, random matrix theory, etc.
  8. @ENE1 I suppose it depends on your goals, but I wouldn't be too concerned about Duke's courses not giving you a solid foundation (although obviously you should probably not go to Duke if you aren't ok doing bayesian stuff). UW has some pretty heavy course requirements and I assume you're comparing it to that, but looking at the syllabi I don't think you'll come out with a much different knowledge base. At both schools you'll take measure theoretic probability and have the option to take stochastic processes. Both schools will obviously have you master Casella/Berger level theory material and take basic linear models classes. Any stat theory class beyond the Casella/Berger level is essentially a special topics course. A lot of programs will teach you basically more theoretical stuff about sufficency, exponential families, etc, while some will have their higher-level theory classes be about bootstrap stuff, while Duke will focus mostly on Bayesian theory. Duke seems to not have an entire class devoted to large sample theory, likely because Bayesian statistics relies on computation more. Insofar as there is a "core knowledge base", all the programs you've been accepted to will provide it. I've had the same line of thinking in the past regarding wanting rigorous coursework, but I don't think that should be a major contributor to your decision. You're going to forget 90% of your coursework in a year if you're not using it in your research. For instance, the poster above mentions that Duke doesn't teach much about GLMs. As someone who went to a program that had multiple semester-long courses on them, that's crazy to me. But 5 years later, I don't remember much about the technical details of GLMs beyond what I could read in 5 minutes of googling. Since all the programs will give you a solid base, I think flexibility to take courses you want and to do research you enjoy are way more important. The syllabi for Washington's classes are all up online, so I don't think going there based on coursework you can access for free is a good decision.
  9. Agreed, an exploding offer before April 15th is a giant red flag.
  10. Yale's stipend for statistics PhDs is $32k/year, and I didn't mention New Haven. $32k/year minus taxes will cover a studio apartment in New Haven, food, with a couple dollars left over. Clearly some programs offer livable stipends, and some offer very generous stipends for the area. But programs at Columbia, Berkeley, and Harvard are not regularly offering >$40k a year. My point is that even these high stipends are only paying for your basic expenses, so accepting a public school's $20k stipend in a HCOL area is going to require lowering your standard of living or taking out loans.
  11. It may be common in all fields for people to ask this, but I'd be really interested in hearing any success stories if they exist, for a few reasons. The department knows the cost of living in the area and offered its standard stipend anyways. There are tons of qualified applicants who would be happy to replace you. Giving preferential stipends to some applicants would create an environment where everybody feels entitled to more money. If the department had fellowships to offer, they've probably sent them out. I just don't see this being a successful strategy, but if you know you won't attend with the current offer anyways, obviously you have nothing to lose and it can't hurt to try. If you do, I'd probably just phrase it in a way that doesn't sound like an ultimatum, that you're really interested in the department but the financial aspect makes it hard compared to other offers you have, and is there any additional funding or summer support available etc Even the Ivies aren't offering stipends of $40k/year, so anyone going to grad school in NYC/Cambridge/Berkeley/LA is going to have to accept a lower standard of living. It does suck that this happens. I was in a similar situation before with Minnesota stats a few years ago, where their stipend was literally like $1000/month and barely covered rent.
  12. Did you get an email saying you were wait-listed?
  13. CMU is a top department and will fully fund you throughout your studies. $3.1k USD per month in Pittsburgh is going to give you a much better quality of life than $20k CAD per year in Toronto - it's over twice as much money with the exchange rate in a city that is a little cheaper to live in. Toronto has some impressive faculty and you can be successful coming out of there, but CMU is probably going to be a stronger department overall. If the immigration concerns are big for you, and if you really like the faculty you're working with, Toronto is a solid department and it would be reasonable to go there. But from the point of view of finances and prestige, CMU might be a little better. QS world rankings, which should be taken with a grain of salt, rank CMU as #10 and Toronto as #14 worldwide.
  14. It's so dependent on the individual department, but generally classes are bigger, you probably won't know most the faculty in a large department, there may be more competition for advisors. For instance, some advisors at NCSU have 10 or more PhD advisees, whereas the whole Brown biostat department has 20 PhD students. As for non-bio/industry positions, absolutely not. A biostatistics degree will be just as good (and arguably better since you may have some more collaboration/applied stats experience).
  15. Obviously I don't think you can go wrong here. On your visits, definitely figure out the ease of working with top faculty. I think Duke probably has an advantage just in terms of the sheer number of elite faculty members working in similar areas. At Duke, you have Dunson, Reiter, Hoff, Gelfand, West, Mukherjee and more- I see these people's papers and former students everywhere in top departments. And since Duke has such a Bayesian/computational focus, I think that might maximize your chance of working with top faculty who matches your interests. This is in contrast to Berkeley or Columbia which have Jordan/Blei, but then not necessarily huge cores of faculty in similar areas. Berkeley and Harvard also have lots of top faculty but in many different areas. Harvard has Meng, Murphy, Imai, Kou -- all great, but working in totally different areas. Berkeley has van der Laan, Wainwright, but again, totally different areas that you might not be interested in. There is more uncertainty there about what problems you'd be able to work on. I think Columbia, besides Blei/Gelman, is probably a step down from the others, but you can dig into specific faculty more to see if they interest you -- Peter Orbanz has similar interests to you, but he seems to have left for UCL recently. You seem to not want to go to Berkeley for family reasons, so I don't think there's any reason you should completely uproot your life to go there. It's not going to give you some big advantage over the other schools. If staying in NYC is important, I think Columbia would be a reasonable choice too. I don't know your financial situation, but Duke is probably going to be the only place there where your stipend will be able to support two people, as Durham is half the cost of the other 3 locations.
  16. I would say the difference between Brown and Minnesota is negligible at best, really. Brown is a solid up and coming department and I think plenty of people would choose it over those other 3. If Brown feels right, go there. I'd think about a couple other things that are as important as research fit, like size, location and funding. NCSU is really huge and would be a totally different experience than a small department like Brown. Even if you wanted to go into academia, I think it could make sense to go to Brown. In industry, it won't matter (some people might even prefer the ivy name).
  17. Some places in Colorado are becoming more expensive, but even expensive parts of Denver/Boulder are much cheaper than cities like NYC/SF/LA. Fort Collins is going to be much more affordable than any major city on a coast. The program itself has a very solid reputation and some great faculty. It is one of the best (maybe the best) places to go if you're interested in environmental statistics. Lots of bayesian stuff too, as well as other fields.
  18. Just so you know, the overwhelming majority of statistics PhDs will never take a course devoted to measure theory in their life. Most PhD programs use a book like Resnick's Probability Path to teach probability with measure theory, which doesn't require having taken a measure theory course.
  19. I've been through the process a few times, and I think the only real way to lower the stress is to lower the importance that you assign to the whole process, which you can do in a few ways. 1) You already have some admissions. Spend some time researching the programs you've gotten into, the city, where you might live. Get excited about one of the opportunities you already have. 2) If you can't get excited about the schools you're already in, think about what you might do if you don't get in anywhere. I'm sure as a smart math-savvy grad, you'll have some other decent options of things you could occupy yourself with for a year. Find a fun alternative. Convince yourself you don't actually need to go to grad school this year and you won't assign any importance to the process. Life is long, and if you realize waiting a year won't be the end of the world, it removes the pressure. I don't think you'll ever get rid of the curiosity and desire to check your email and see results, but you can at least lower the unnecessary stress. I've worried myself sick about this stuff too many times and it's not worth it. Your life will be fine regardless of these results and you really need to convince yourself of the truth of that.
  20. @MrSergazinov OSU is very Bayesian and like half their department works on Bayesian computing. TAMU has a few people like that too. Like @Geococcyx said, Katzfuss is a good example of how most people doing "computational statistics" are working in other methodological/applied areas too and doing computational work that helps accomplish that. So I'd dig a little deeper into the faculty members at each and see what exactly interests you about their research.
  21. There is not an issue, really, just a matter of preference; there is nothing wrong with TAMU or the department that I know of. American college students, especially those who go to graduate school, tend to be politically liberal and want to live in cities on the coasts. Just the other day, someone on this forum posted a profile review saying they didn't want to attend a school in the Midwest. On top of the geographical reasons, TAMU is in a small town in a politically conservative state. The school has a large ROTC (military training) program, the town residents are largely evangelical Christians. It is not the type of place that young, liberal people generally would like to live because there isn't a ton to do there and people are different than what they are accustomed to (compared to a town like Austin an hour away).
  22. While I think TAMU probably has stronger faculty despite the rankings, I think TAMU struggles to recruit domestic applicants because of the location and culture of the school. I'd expect Madison to be significantly harder to get into.
  23. Historically, if you are admitted on the fast track with funding, you do have to reapply for the PhD but you are essentially guaranteed admission, provided you do ok in your classes. But it is still an added stressor. If you're confident in your ability to be a good student, I wouldn't be overly concerned about this. There is also the advantage that if you don't like it and want to leave with a master's or transfer to another program, you are reapplying for PhD programs after 2 years anyways, so it creates a less awkward way to leave.
  24. I know from personal experience they have historically they have given at least some PhD applicants admission in late January.
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