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  1. Internships do not matter much unless you produce high-quality papers, which is rare for undergraduate students. Your ability to do mathematical proof is the most important so I would take proof-based courses in mathematics or statistics.
  2. How does Indian GPA translate into US/Canadian ones? If your 74% is equivalent to an A- or above and you do well in real analysis/measure theory, you may have a shot at schools in the 30-50 range such as Florida State/UConn/Ohio State and tie-2 schools in Canada such as Simon Fraser/Western Ontario. From my experience, the subject test is pretty useless unless you aim for Stanford/Chicago/Wharton so I wouldn't take it.
  3. If you retake these prerequisite courses and get good grades in them, I think you have a chance at mid-tier master's programs. The general GRE test is mostly used as a filter so you should be fine as long as your score in the Quantitative section is above the 80th percentile. Beyond calculus and linear algebra, the subject test also covers basic real analysis, complex analysis and abstract algebra and I doubt MS programs care about the test. Without a strong pure math background it is impossible to do well so I wouldn't take the subject test.
  4. Master's admissions are much less competitive than PhD's since you pay for the degree. With your profile, if you can get your GRE Q to 166+, I think you should be competitive for the top 10s. I would definitely apply to top schools like Harvard, Berkeley, Chicago and Stanford as well as add some safer options in the top 20s.
  5. If you haven't taken any proof-based courses, I don't think it's a good idea to take grad-level probability courses. I would start from basic real analysis courses. Ideally you need two courses in real analysis covering material such as metric space/norm, completeness of metric space, hölder's inequality/Minkowski inequality(this can be further extended to other spaces such as Lp space and probability space) , basic topology, continuity/uniform continuity in metric space and Riemann integration. It would be better if you had exposure to Lebesgue integration before taking grad-level probability courses since probability space is a special general measure space and understanding Lebesgue measure definitely helps.
  6. Since you are from a top school that is known for grade deflation, your grades should make the cutoff for the schools you listed. In order to have a better chance, I would take real analysis this fall since it is going to be helpful even if you do applied research. If you scored an A- or above, I think this would be a positive sign for admissions committee. I would apply straightly to PhD programs and add a few MS programs.
  7. I wouldn't recommend you take a gap year because nobody knows what the situation will be like in 2022. Since you haven't taken abstract algebra and complex analysis, it would be very time-consuming to prepare for the test. Unless you are dead-set on getting into Stanford, I wouldn't recommend taking the math GRE. You have excellent math grades from a good school, so you should be competitive at the top 15 programs in US News if you can secure strong letters. I think some schools accept fall grades and you could submit them if you do well, which I think will help your case. As for program selection, aside from the top 15 schools, You could also add a few larger and lower ranked programs such as Penn State, Purdue and UIUC just to be on the safe side. Given the current political climate in the US, international student visas are harder to come by so I would also consider applying to a few top Canadian programs such as Toronto/UBC/Waterloo/McGill.
  8. I wouldn't worry much about your low grades in general ed courses since they are less important. You have good math grades from a top school and if you can improve your GRE Q to 166+, you should be able to get into some top 20s. You may even have a shot at schools like Michigan/CMU/Duke. Admissions for biostatistics/OR is somewhat less competitive than statistics and I can see you get into really good biostat/OR programs. For example, Northwestern and Cornell have a very good OR program and I think you have good chances at those. Meanwhile, you could also consider top schools in Canada like Toronto and UBC.
  9. Your mathematics and statistics background is lacking and I am guessing your undergrad institution is not super famous. With that said, I don't think you have a shot at the schools you listed. If I were you, I would first do a master's in statistics and take real analysis and mathematical statistics. If you do well in your master's, you probably have a shot at schools at the level of UGA/Michigan State.
  10. Thank you so much for your advice. For anonymity reasons I won't say exactly which school I plan to attend but it is one of McGill, Waterloo and UBC.
  11. It appears that the description of my last poll is misleading and the result may be biased, so I am reposting the last poll. I would like to hear about your opinions towards the above programs. If you were admitted to these programs, which one would you choose?
  12. Thank you for everyone's response. I have no prior experience in machine learning but I am open to research areas I've not yet considered. It appears that ARWU places Waterloo ahead of UBC and McGill while QS statistics ranking places UBC and McGill ahead of Waterloo. Which ranking is a better proxy of the academic reputation?
  13. Although I did causal inference research during my master's, I'm also open to other research areas. I have found areas such as functional data analysis and spatial data interesting. McGill's Biostatistics department seems to be pretty small and focused and I prefer larger departments with more research areas.
  14. Seems that many people vote for UNC. Is it worth it to turn down a funded offer and accept UNC, which does not guarantee funding?
  15. I am fortunate to have been admitted to the Statistics PhD program at UBC, Waterloo and Biostatistics PhD program at McGill, UNC (unfunded) and Florida and I am having a hard time making a decision, so I would like to seek out advice from you guys. It seems that the academic placements at UBC and UNC are very good, and McGill is a better research fit as they have a group of people doing cutting-edge research in causal inference. Florida is relatively new but there are a few faculties I would like to work with. Waterloo has a large statistics department with a focus on applied side. Obviously UNC has the best reputation but I don't think it sensible to pay for the tuition and living cost for 5 years.
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