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StatsG0d last won the day on October 29 2020

StatsG0d had the most liked content!


About StatsG0d

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  1. I did an internship in front office quantitative finance and ultimately I realized the position was really a glorified software programmer. Sure, they used statistics (although I was once told to “do data science”), but the job was really about developing software to create nice plots. At the end of the day, whatever you do as a data scientist / quant has to be explained to a larger audience who will know very little if anything about statistics / ML All this to say: you can’t judge a job by the title / job description. And I’ll echo the others and say the vast minority of my time is ac
  2. If you're doing research, I think it's crucial. In general, you want to prove some asymptotic properties of your method, whether it be it's consistent or asymptotically normal what have you. The NYU data science PhD program requires a sequence in probability and statistics. One component of the course is convergence, so these topics will definitely come up again. Stuff like this is, in my opinion, somewhat insulting. I realize that you are not intending it to be so, but note that many of us (but not me) on here have devoted our entire careers to asymptotics and they are importa
  3. Power and sample size can become quite sophisticated (see, e.g., the literature on probability of success / assurance / Bayesian power). I'm surprised to hear about the not doing imputation though. I imagine that would raise the eyebrows of regulators if there are a lot of missing data. I don't know the size of the place you're at, but there are many people working in pharma that are extending clinical and regulatory science. Sounds like you might be at a smaller place or a place that doesn't encourage such work? Forgive me if I implied this. My point was that uncertainty quantificatio
  4. I'm not sure this is fully true, as tech companies are really more focused on machine learning / AI than statistics IMO. I guess it depends on what you mean by "real" statistics. To me, real statistics is about quantification of uncertainty, and that is the primary difference between (bio/)statisticians and ML folk. The regulatory constraint is actually more of a methodological interest than a limitation--how can we maximize power / the likelihood of approval subject to the analysis constraints that the FDA sets. Topics such as type I error / multiplicity become increasingly important
  5. I don't agree with this at all. Wake Forest is a very reputable school and there's a list of institutions that their Master's graduates end up attending, many of which are very prestigious. Not sure if you're trying to actually take a knock at Wake Forest in particular or if you were just oblivious to this fact. I think the OP has a great chance at top-20 programs and a small but nonzero chance at a top-10 stats. I could see them getting into any/all of the top-5 biostats programs. Their mathematics knowledge is extremely deep--far deeper than the vast majority of domestic students. They
  6. I think if you explain in your statement of purpose the reasoning behind the grade it's definitely not a deal breaker. I think adcoms know/understand that it's difficult to learn in a virtual environment.
  7. Yep mostly this (although I do feel like this has been changing in recent years). The top-4 programs are known to be pretty mathematically rigorous. Perhaps not as much as stats departments, but much more than the rest of the bunch. I think there are some schools that will automatically consider you for MS admissions if you don't make the PhD. You could target those schools. Also, a lot of the programs have a process for internal PhD applications (e.g., I know UNC does). So you could also apply for an MS at a program where you'd like to do a PhD and see how that goes. I feel like that's a
  8. You'll probably get into all of those MS programs. In fact, I think you're competitive for any MS program in stats in the country, so take your pick. You might be able to get into some PhD programs ranked 30-50 directly if you're interested in that. This is particularly true if your school is known for some type of grade deflation that would bring your GPA into some light. While the GPA is somewhat low, you did go to a very good undergraduate institution and have a pretty broad math background. I'd be shocked if you didn't get into at least one stats PhD program 30-50 or biostats PhD
  9. I had a master's degree from a top-30 stats program, where I took several PhD level courses (Math Stats I-II, Linear Models, Generalized Linear Models). My friend (who is in a stats dept.) from the same school had all those courses + PhD-level measure theory and probability theory, limit theory, and several graduate-level courses in the math department. Sadly, we both had more rejections than acceptances.
  10. I do not typically recommend taking PhD-level courses, because I think it calls into question why you would not just stay at the university in which you're taking these classes. Plus, I think some departments can be turned off because they want to mold you into the researchers they see fit--they don't want you coming in with *too* many ideas / opinions of your own. This is just speculation, but it certainly seemed to be a case with me (for biostats) and a friend (for stats). This is anecdotal, but still.
  11. IMO, what will boost your application the most is none of the above. If you are able to, it's better to take upper-level proof-based math courses, even at the undergrad level. The biggest doubt of your application is your math ability. Consider taking courses like (in no particular order) Number theory Abstract Algebra Complex analysis
  12. I think it can matter for Finance companies if you're interested in quantitative finance (who typically only recruit from very prestigious universities), but otherwise I don't think it matters at all.
  13. This is a very interesting and atypical profile. First, if you obtained your MS at least 2 years before you will be submitting applications to PhD programs, I highly recommend applying for the NSF GRFP and writing about that in your statement of purpose. Not a deal breaker if you can't do it, but it will tell admissions committees that you're very serious about research and have thought deeply. Based on your background and credentials, I think you would have a good shot. Now, although you did not do well in undergrad, you have very good grades in graduate level stats courses, which is gre
  14. I think you might have a (relatively small) chance at Harvard, but probably not Hopkins. The other schools seem very attainable. Maybe add UNC/Michigan? They seem like good target schools for you. The biggest detriment to your app is that your real analysis grade won't be in by the time you have to submit. Do you have the ability to take real analysis in the summer? An A in real analysis would be a huge boost to your profile IMO.
  15. It's probably much more difficult as an international student, but FWIW I did an undergraduate degree in Economics and I was admitted to nearly all the schools I applied to in the 10-20 range.
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