galois

Wondering if anyone can compare these two programs. I think UW's MS probably doesn't get much attention since its actually a Professional Masters Program (PMP) and only available to local candidates. It's one of those "not a road to the PhD" programs. I'm here in Seattle now after joining a PhD program at another department at UW, but second guessing that decision, and considering bailing and just doing a masters in CS. UW's program is pretty flexible in that you can probably do it remotely without much trouble. Columbia offers their MS through the virtual CVN as a distance program which is also convenient. Aside from price & time to graduate, does anyone have experience with classes from either program, and the actual quality of education? I worry that the PMP (which does not really have pre-reqs per class) will be too cushy. And I just have no idea what to think of the Columbia program as it's hard to find individual accounts of people who've done it.

I don't actually typically like watching lecture videos, but I'm enjoying this one. It may just be the nice separation of Strategic Practice / HW that I find so encouraging for self-study. Anyway @bayessays thanks for the advice, I think I needed to hear that. I've been stressing out, but in reality I should probably just chill out and enjoy free time while I have it. Maybe I'll just check out the fast.ai course casually once I'm done with 110. Thanks all!

Hey all, first off just want to say thanks again for the Stat 110 suggestion - it has been fantastic! I'm on course to finish it before the semester starts, and I wanted to reach out for other suggestions. I have also purchased the Schaum Calculus book as suggested (and have been slowly motivating myself to do exercises). I was trying to think of other things I should quickly prep before school, since it's been a while. I was thinking a review of computational linear algebra and real analysis. Anyone have recommendations for something I could squeeze into ~ 1 or 2 months part time? I just came across All the Mathematics You Missed on amazon, seems geared towards entering Math PhD, but curious if anyone has opinions on it. Also, is Stat 110 a typical "probability" undergrad class, or "mathematical statistics"? These seem to be separate courses at most undergrad institutions, and I'm missing the context of where this lies in a typical math/stat education.

Well I might not be the best person to answer this question, but I'll give it a whack. I would say things like calc/linear-algebra fall into the "prerequisite"s that of course you need to have taken, and things like optimization/ODE, while not prerequisites, are almost "nice to have prerequisites", in that it doesn't hurt to be exposed to some of those computational concepts. I think when adcoms like seeing "math" they are referring to rigorous, proof-intensive courses that you would generally take as a junior or senior, things like real-analysis/abstract-algebra come to mind. Obviously, real analysis being the more important of the two, since that is actually relevant to the theoretical courses you will take in graduate statistics. But, even though something like abstract algebra probably isn't relevant at all to what you are studying, getting an A in such a class would demonstrate your ability for very abstract problem solving and theorem proving.

Undergrad Institution: Top 30 Liberal Arts College Major: Mathematics Concentration: Scientific Computing GPA: 3.91 Type of Student: Domestic Mixed-Race Male GRE General Test: Q: 167 (98%) V: 168 (94%) W: 4.0 (59%) GRE Subject Test in Mathematics: N/A Programs Applying: Statistics/Applied Math Research Experience:  - REU in combinatorial mathematics: presented at JMM, won a poster presentation award, resulted in publication  - REU in gravitational physics: worked in a lab in Paris doing some experiments and analysis on a table top simulator for LISA mission. Awards/Honors/Recognitions: A fair number of awards and scholarships in undergrad. Pertinent Activities or Jobs: Lead tutor for Calculus I-III for a few years. Software engineer for a number of years now. Letters of Recommendation: All three from the math department, and very likely strong letters. Math/Statistics Grades: Calculus I-III (A), Intro Statistics (A), Linear Algebra I-II (A), Abstract Algebra I-II (A), Number Theory Seminar (A), Mathematical Logic Seminar (A), Complex Functions (A), Real Analysis I (A). Any Miscellaneous Points that Might Help: Software dev job experience; no stats background. Applying to Where: Caltech - Computing & Mathematical Sciences PhD / Rejected U Washington - Applied Math PhD / Rejected (accepted to masters unfunded) U Washington - Math MS / Admitted Carnegie Mellon - Stat PhD / Rejected Duke - Stat PhD / Rejected UNC Chapel Hill - Stat PhD / Rejected UT Austin - Stat PhD / Rejected U Washington - Stat PhD / Admitted NC State - Stat PhD / Admitted UCLA - Stat PhD / Admitted UC Irvine - Stat PhD / Admitted U Florida - Stat PhD / Admitted I really had no clue what to target when I started the process of finding programs. This forum was incredibly helpful in finding a good spread of safety-target-reach schools. I was rejected/accepted by exactly half/half of the programs I applied to, which seems like a good result.

I'm surprised about UNC as well. IMO if students are on your waitlist, you should at least make contact with them at some point prior to April. I can only see benefits to doing that, where are the drawbacks??

I just turned down NCSU, UCLA, and UC Irvine. Hope that helps any of you that are on their waitlists! Good luck

In choosing my program this year, I wanted to be sure that there were at least a few faculty members that I was interested in working with that would potentially be interested in working with me. So before I made any final enrollment decision, I reached out to a couple of profs and asked if they were planning on advising new students from my cohort. This is a bit less forward, but they know what you're getting at, and in any case this is very valuable information; in my case one such professor replied no! That's a good thing to know beforehand.

I would err on the side of showing interest rather than avoiding being a nuisance. Meaning, I would email sooner rather than later. But, I don't work in adcoms, so take it with a grain of salt.

I was in your situation this past cycle, but more severe. The only stats course I took was 101 freshman year. The rest of my courses were pure math, like logic, algebra, and analysis, not even probability! I still got into some great programs. At visit days I learned that it's far more common to have a pure math background, although typically students have seen some level of mathematical statistics, so I do think I'm still in the minority, but I'm not quite as odd of an applicant as I initially thought. A professor at UW explained to me "we look for people that can learn statistics", as opposed to people that know statistics. So, a background in pure math + graduate statistical theory class + research in probability and random processes actually makes you an ideal candidate from what I can tell.

Don't know if anyone else is waiting on Caltech CMS, but finally got a response from the program coordinator there; they are finalizing offers and applicants should expect a response next week. I haven't been contacted or interviewed by anyone there, so I have 0 chance, just thought I'd pass the info along.

I have typically learned math through textbooks but that stat110 looks like a very convenient format, especially given my time constraints, so I'll probably do that. Thanks for the advice. For Schaums, I imagine since it's so popular, even if answers aren't in the back of the book, they're probably easy to find online?

I just spoke to a professor at a program of interest and asked what I should do to best prepare for my coursework but also the type of research that professor does. The most important thing emphasized was to know my probability very well. I never even ended up taking a formal probability class in undergrad! So I'd like to know if anyone has experience with a textbook that is particularly good for self-study. I tend to like books where the exercises are proofs to write; I'm not sure if this will be the same in probability, perhaps it is more of a computing type of problem solving, but thought I'd mention it. For example, I'm currently reviewing linear algebra via "Linear Algebra Done Right" by Sheldon Axler and it is fantastic. Also, if someone has a good source for calculus problems that would be great. I think instead of a textbook I'd rather just have a lot of tough derivative/integral problems and work through them until I remember all the relevant bits.

I have not. I think UNC and Caltech are the only ones that haven't gotten back to me yet. I'm just assuming rejection for now.

I was at the visit day as well; I thought during the Q&A we learned that basically everyone passes, at least by their second attempt, and the few people that do fail are those who are choosing to exit with an MS (and thus don't intend to pass). Am I mistaken?