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DanielWarlock last won the day on September 28

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About DanielWarlock

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  1. You have an excellent shot. I personally am studying at the Harvard program and one of my best friends in college studied at Princeton ORFE. I don't think my friend's or my profile is much stronger than yours. The only thing is both of us get genuinely strong letters (with preprints) and nearly full GPA like you. If your letter is as strong as you say (looks like it will be), then you can crack these top programs.
  2. I actually digged into the origin of Chatterjee's lecture notes as it is still quite bizzarre that he comes to know so much physics out of nowhere. It turns out he was following a set of notes by Talagrand himself who took up learning quantum field theory as a hobby after turning 60 year old. In the preface of those notes, Talagrand described his frustration of learning physics as a mathematician which is quite funny.
  3. I don't have a clear answer to this. But I just want to comment that it can become extremely broad. For example, I recently discovered that Chatterjee at Stanford even works on quantum field theory! (https://statweb.stanford.edu/~souravc/qft-lectures-combined.pdf) It makes sense in that a lot of statistics come from physics. But eventually I think people just do things because they find them interesting.
  4. Yes I do think you have an excellent chance. At least at Harvard I know there are much interests about sports analytics. You will most likely get in to be frank if you elaborate that in your letter. I know Mark Glickman and Kevin Radar are involved with data science program at SEAS but I'm not sure to what degree. Take a look at their work and mention that you would be interested to work with them and the sports analytics club in your letter. I say you have very very good chance to get into Harvard. Stanford statistics master is hard to be admitted and the coursework is rigorous. ICME on
  5. This is a very solid list, although I think MIT and Princeton OR programs, Stanford ICME, Michigan data science are worth consideration also.
  6. I think the best first year plan is probably Columbia. They have 4 different tracks: probability, theoretical statistics, applied statistics and data science (joint with CS and managed by Blei himself). Students take different classes (with some overlap) and take different qual exam. This way, no one will waste time. Coursework looks to me very rigorous and in-depth within each track. For instance, if you specialize in probability, the probability sequence is 3 semester instead of 2. Other than this, it is also good to have a more hands-off approach such as Harvard where courses do
  7. We used Agresti foundation of linear and generalized linear model. I feel that it is a bit cumbersome and wordy but in general a good book in terms of mathematical rigour. A lot of details regarding different models are not useful for me but I did learn some very useful techniques and practiced calculation quite a bit.
  8. To master a technique for me is very very hard. In fact, I often find that taking even a very solid class does not truly allow me to master a technique--in the sense that I can independently solve a problem using that technique. To give an example, I first learned the gaussian interpolation in a class in the context of Slepian lemma. Then I read Vershynin's book and learned it again, this time not only Slepian but also its extension such as Gordon's inequality. I even derived Gordon's inequality using interpolation as an exercise from the book. Now when I see it again in the context of sp
  9. Contrary to popular belief, I feel that 1st classes at my stats department uses very minimal real analysis. The prerequisite for almost any class is just linear algebra and calculus. You can literally know zero real analysis and do pretty well. But a level of mathematical maturity is always assumed. It is mostly about problem solving rather than actual knowledge. A CS major, if solidly done, should have absolutely no problem. A biology major will be more challenging (I'm not talking about "biologists" who are actually theoretical mathematicians or computer scientists in disguise).
  10. Unlike place like Stanford, these programs mainly concern how well you are connected with Toronto risk management community, particularly your internship records. If you do not have necessary connection and experiences, they are indeed quite tough with just gpa and research projects. Regarding internship pay, it is very true, and apply to most technical master programs in US also. If someone does not follow up with a phd like myself, a master turns out to be a pretty sweet deal that pays back within a year or two. Of course this is conditional on that you have initial fund to pay tuition
  11. I'm very familiar with those programs. I know most people on MMF admission and worked with several people from MQF. First of all, MMF and MQF are both unfunded and has zero research component. They are very competitive because they are well-connected in industry and can guarantee 100% you will find a high paying job in finance after graduate. Your friends didn't get in because it is very hard for to get into MMF with no connection. If you don't know anyone on the committee or have already worked with Canadian banks such as RBC, scotia, or pension funds such as CPP OTPP, there is practical
  12. Certain funded masters or phd programs in Canada explicitly ask you to contact advisor before application, because funded means mandatory research. The program wants you to have a match with faculty before they take you. See for example uoft mathematics phd or MASc (if I recall correctly). Sometimes this is not written explicitly on the website but is assumed. You still didn't specify which funded master programs at UofT and waterloo your friends applied to so it is hard for me to judge the truth. That said, I do feel Berkeley is easier to get into compared to Chicago (I'm specifically referri
  13. I get downvoted for a million times here for saying some elite master programs are very competitive. The dogmatic opinion on this site is that master programs are easy to get into, often described as much much easier than most phd programs. But it is simply not true. The aim of my previous post is to explain why. Also note that most posters here are direct biostats/stats phd applicants so it makes sense there is a bias. I also want to add that elite master programs like Stanford tend to be very competitive is a fact that bolstered by their admission stats. Not everyone wants to do or dea
  14. It depends on the program. Some Canada programs require networking beforehand. If you just apply out of blue, you get rejected 100%. Can you give specific situation of your "counterexample"? Are those same programs OP mentioned because I applied to the programs OP mentioned.
  15. US masters at prestigious schools can be harder to get into than funded masters such as those in Canada. I was accepted into UBC and Waterloo but was rejected at Chicago and Stanford--both are self-funded programs. The reason for this is simple: most graduates from these schools manage to land jobs in highly prestigious firms such as Google, Facebook, Apple, Citadel, Goldman Sachs. The school itself provides lots of opportunities in the form of alumni and career fairs. Some even find full time jobs in the same group that hires PhDs and working on exactly the same stuff (stuff that one may con
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