Casorati

You have a good math background and a strong overall profile. The schools you listed are reasonable and I think you have a good chance of getting into some of them. However, admissions to PhD are very competitive. That said, I would apply broadly and also add some safe options. I think 10-20 schools are a good target to go. You could apply to some of the top 20 schools, some midrange schools like UIUC/Davis and some safety schools in the top 50s.

You should directly go for PhD if your goal is a PhD. Your grades and mathematical background are very impressive. If you are from a school that is known for grade deflation, your grades in grad analysis won't hurt you, since they are hard courses and students in those classes are usually very smart. I would say that you have a very good shot at top 10s and even top 5s. Just to be on the safe side, I would also add some schools in the 20-30 range such as Penn State/Minnesota.

You should directly go for PhD if your goal is a PhD. Your grades and mathematical background are very impressive. If you are from a school that is known for grade deflation, your grades in grad analysis won't hurt you, since they are hard courses and students in those classes are usually very smart. I would say that you have a very good shot at top 10s and even top 5s. Just to be on the safe side, I would also add some schools in the 20-30 range such as Penn State/Minnesota.

Completely agree with this assessment. I went to U of T for my undergrad and also had background from finance. I did my masters at Harvard with full A's in standard phd sequence (math stats, probability). My GPA is much better (near 4.0), with strong letters. Still I was rejected at schools at the rank of ~50, e.g. University of Florida as well as mid-ranged schools such as UWM. A major flaw is my math background which is still stronger than yours. The point is mid to low ranked schools care A lOT about math abilities such as real analysis but I don't have it. 
You are definitely NOT safe at ~50 rank level. And I would say UT Austin, Penn state, UWM level school is the "pipe dream"/"top choice" level.   

I doubt that one course in real analysis will change things drastically. I had overlapping courses with actuarial students at UofT including the "Elements of analysis": MATH 336 H1. This is the real analysis class for actuarial students at uoft and I'm worried you may take it. Don't! Take MATH 357H1 instead. I also had complex variable (334) btw. Got 100 in both of these classes --no help to my application at all. The truth is that most people in those classes are definitely not math-savvy and have no clue so the instructor has to go extremely slow and review calculus stuff all of the time. The most advanced thing we learned was just calculus materials like sequence convergence, series, mean value theorem and we don't learn those very well.   In fact, a lot of classes at uoft is made easy and "useless" for you future academic careers as a PhD in stats, except for those for pure math specialist: e.g. MATH 357H1, Math 347H1.   MATH 336 H1 don't even teach standard real analysis material like Azera-Ascoli, Weierstrass but 357 does. I thought admission at other schools don't know the difference but I was wrong. I was immediately questioned for taking "computation based" math classes. Someone even said I would be better off taking hard, proof-based classes with a less perfect score. Absolute truth. I got 95% on my linear algebra class (designed for engineers)--I didn't even understand eigenvalues beyond the definition. So you see how those marks you got on your transcript are questionable . 
Also a definite way for us non-math majors is to take GRE math subject tests. I can tell you that it will definitely help boost your profile if you score anywhere above 90%. A hard task but getting high mark is not the only objective. I took it twice with one year span in between. Did poorly both times (74% and 79%) so didn't end up submitting it. But I don't regret studying for it one bit as it really prepares you for grad school if you are not solid in calc and linear algebra. Similar to you, I worked in risk management and most my work consisted of excel and writing simple programs. Taking GRE math really taught me calculus and linear algebra before grad school. I self-studied from classic books like linear algebra done right, baby Rudin, Dummit and Forte, Munkres etc. Of course, a "crash education" in math like this is not comparable to a 4-year, solid math education but it's absolutely helpful for my grad school and allowed me to read some theoretical papers.  

For master's programs in the US, real analysis won't matter that much and many people get in without having taken it. Most master's programs in the US are not very selective probably except for a few elite schools like Berkeley/Chicago/Stanford. However, statistics master's programs in Canada are much more selective because most of them are funded, and if you perform satisfactorily in the program then you are almost guaranteed to transfer into the PhD program. With that said, your lack of real analysis, low grades in a couple of statistics/math courses, along with your undergraduate institution might have made you less competitive at UBC/Toronto. Taking real analysis and obtaining good grades in them would definitely help your chances, and having a strong math background never hurts, especially if you consider a PhD in the future. However, even if you get strong grades in real analysis, UBC/Toronto are still gonna be reaches. It's just that admissions for top master's programs in Canada are very competitive. For example, UBC had 247 master's applicants in 2019 and admitted 15 of them. If you were to reapply, I would suggest also applying to schools at the level of Simon Fraser/Western/Alberta, which I think you have a good shot.

For master's programs in the US, real analysis won't matter that much and many people get in without having taken it. Most master's programs in the US are not very selective probably except for a few elite schools like Berkeley/Chicago/Stanford. However, statistics master's programs in Canada are much more selective because most of them are funded, and if you perform satisfactorily in the program then you are almost guaranteed to transfer into the PhD program. With that said, your lack of real analysis, low grades in a couple of statistics/math courses, along with your undergraduate institution might have made you less competitive at UBC/Toronto. Taking real analysis and obtaining good grades in them would definitely help your chances, and having a strong math background never hurts, especially if you consider a PhD in the future. However, even if you get strong grades in real analysis, UBC/Toronto are still gonna be reaches. It's just that admissions for top master's programs in Canada are very competitive. For example, UBC had 247 master's applicants in 2019 and admitted 15 of them. If you were to reapply, I would suggest also applying to schools at the level of Simon Fraser/Western/Alberta, which I think you have a good shot.

I think you probably have misconceptions on what PhD is about. At the PhD level, you dig deep into a particular area and conduct original research, where you would need a deep understanding of mathematical/statistical theory. If you don't have very strong mathematical skills, you are gonna have a hard time in your PhD coursework such as probability theory/inference (sorry but I don't mean to scare you), let alone making breakthrough in research. Given your B/B+ in undergraduate math/stat courses, a question you wanna answer is that if you are confident of doing well in real analysis and other proof-based courses, which are much more challenging than the math courses you have taken.

I guess you have a good background in math. But I can not see Multivariate Statistics, Bayesian, and GLM(or categorical data analysis) in your coursework. Some computing courses on SAS, R, Python, and Database might help too. If you are worried about the reputation of your school, having extraordinary LORs are really important. Seems like your GRE scores are high enough to not be automatically screened by the top 20 schools. For SOP, you can refer to here. If you write an excellent essay with the fine language control of English and find a perfect fit for your interests, then you can also be one of the top choices for the programs. To sum up, I highly recommend you to take some more statistics courses and contact your alumni who got into the top statistics programs ASAP. They could recommend you some professors you can ask a help for.

I have a friend who is attending PSU's statistics PhD program. According to him, the coursework at PSU is indeed intense and each term he takes three courses and each course has weekly assignment. He has to take two full years of very theoretical courses. I am now attending one of the top statistics PhD programs in Canada and I only need to take 5 courses and I can choose whatever courses I want to take. I choose probability this term and plan to choose a second probability course next term. They are challenging but I found them extremely rewarding.

TLDR: All those programs are pretty similar. If McGill is a sure thing, it's probably only worth it to apply to other places if you would for sure go there over McGill.
These rankings might help (but they include OR so there's some noise). All of those programs are great. The ones @Casorati mentioned are all really good. I would put them in the same tier.
Provided you get into at least two of these, I would say go wherever you feel you would be happiest. If it were my choice, I would say McGill simply for the location. Sure, it's cold, but so is West Lafayette, Urbana, and State College. At least McGill is in a large city and there will be a lot to do / easy to catch a flight somewhere. UNC is kind of a nice middle ground because while Chapel Hill is a college town, a major airport (RDU) is only a 20-minute drive, and Durham and Raleigh are both nice cities with quite a lot to do relative to their size.

I don't think these schools are much better than McGill either. Maybe Purdue/UIUC are comparable to McGill. I would say schools like Wisconsin/UNC/Penn State are a tier above McGill.

Hi, all!  
I'm new here ?
I'm graduating from Russian top master's program this year, and wish to apply for Ph.D. in Computer science in US for fall 2021. I am interested in Computer vision. I am really looking for help from kind people of GradCafe with my profile evaluation. Particularly, I am worried about my low GPA at bachelor. 
 
Bachelor institution: top-ranked Russian university. GPA: 3.0/4.0 
Major: Data Analysis
 
Master institution: other top-ranked Russian university. GPA 3.92/4.0 
Major: Computer Science
 
IELTS - 7.0
Type of Student = Muslim male
 
Research experience: 
working as part-time researcher at my masters supervisor's lab   1 summer research internship at top Russian research lab in computer vision., Work experience: 
1 industrial 6-month l internship  one year as Computer vision engineer at photo-editing app start-up  
Publications: 2 so-so papers (one is first-authored)
LoRs: 
first is very strong from my current research supervisor,
second is from my summer internship supervisor, who is well-known in the field of computer vision. but we did not interact much, so he can't say a lot about  me
third is good from other senior researcher, I worked with
Teaching: have a lot of experience in teaching as Lead TA and Lecturer
 
Schools I like:
(top) University of Virginia, UT Austin
(medium) UIC, Northeastern university
(low) Rochester institute of technology
 
How are my chances? Am I good enough for these schools (at least medium and low ones)?
 
Thanks in advance!
 

I agree with @Casorati. Northwestern's program honestly is not great. Evanston is a really nice place and near Chicago so location it's awesome, but the program prestige is pretty lacking.

Northwestern has a very small statistics department and it might be hard to get in because of its general prestige. Rutgers has a good statistics department with a wide range of research areas. However, I still think McGill is a better option than Northwestern and Rutgers. 

I would assume you attended McGill for undergraduate and master's. If you are pretty sure you can get into McGill's PhD program, it only makes sense to apply to better schools than McGill. With that said, I would not apply to SFU and Pitt. Biostat at Duke and Brown are relatively new and despite their low ranking, it would still be difficult to get in because of their general prestige. Other schools in your list are reasonable targets I think. Given your strong performance in advanced math courses, I think most schools will overlook your D in linear algebra and B- in intro statistics so I wouldn't worry too much about it. 

Biostatistics is still statistics with more emphasis on medical data. The bio prefix is misleading since biostatistics has little to do with biology. I have not taken a single course in biology and I still did and probably will do research in biostatistics.

Many schools have made the GRE optional this year so you don't need to submit the score if that's the case. For other schools, 157 is indeed too low and I would shoot for at least a 160.

Everyone will go to the best possible school he/she can and most professors would be happy if you end up going to a better school and it's not unusual to apply to many schools.

You could ask your current supervisor at Waterloo if he/she is willing to take you as a PhD student. If your goal is to obtain an academic position then I don't think Toronto biostatistics is a good fit for you. It's a very applied program and the academic placements are pretty bad.

I often find that the best way to learn a new field/subject is to watch video lectures, read review articles and read select chapters from textbooks. So when I wanted to learn about variational inference, the first thing I did was watch a few video tutorials by David Blei and Tamara Broderick. After establishing this "baseline," I kind of just pick up on things as I go -- i.e. I just read the papers and try to figure out what the authors are doing as I go. This gets easier to do as you gain more experience and as you read more papers (in the beginning, I might annotate the papers a lot more). 
Realistically, when you are doing research, you won't know (or need to know) *everything* there is to know about, say, convex or nonconvex optimization. But you can pick up what it is you need as you go, and if you encounter something you're not familiar with, you get better at knowing WHERE to look and fill in those gaps. 

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). 
 
 

Your mathematical background is too light to apply for PhD in statistics. At minimum, you should have done one course in real analysis and many applicants have taken much more proof-based courses. Admissions committees care most about your grades in math/stat courses so your undergraduate GPA won't carry much weight. However, your graduate GPA is mediocre/below average given the grade inflation in grad school so that also doesn't help. With that said, most schools you listed are unrealistic. I am attending one of UBC/McGill and most students are math/statistics major from top universities in Canada or USTC, Zhejiang University in China, so I don't think your odds are good at UBC/McGill either. I would mainly apply to biostatistics PhD programs ranked below 50 since lower ranked biostatistics programs are more applied and more lenient on math requirement. The Biostatistics PhD program at UToronto is very applied and I think you have a chance to get in.

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.

Your low GRE Q, along with B's in real analysis will raise concerns about your ability to do math. With that said, you may have trouble getting into top 10 Biostat PhD programs. Taking complex analysis/abstract algebra is not absolutely necessary unless you are very interested but I would consider taking measure theory. If you do well in measure theory and raise your GRE Q to 165+, I think you have a chance of getting into schools like UNC/Michigan.