MLE

UC Berkeley, UW, and U Michigan are hosting a (virtual) panel discussion with current Statistics PhD Students on Wednesday, November 3rd, 2021, at 5PM PST/8PM EST. You are invited to bring questions regarding graduate study in Statistics and related fields. Learn more and register at StatsPhD.com. In case you can't make it Wednesday, the panel will also be recorded and posted to the website!

If you are intent on going to a PhD program in stats, you may want to add some more safety schools to the list. Outside of UCLA, which I am less familiar with, I think all of those programs are quite selective. Edit: UCLA's grad admissions data shows their Stats PhD admission rate at around 10%.

@PLessPoint05 11:59 pm in the timezone of the school, per this source

I gotta disagree a bit here. There's two professors with joint appointments now (excluding Fox who is now at Stanford), one of which never is at the department and I think has a 0% appointment now, the other has like one CS student. Outside of that one professor, the interaction between the stat and cs departments is pretty non-existent. I don't believe there are any other active collaborations, seminar attendance is disjoint, and students in one department generally don't take courses in the other.

If you think UCLA may give you an offer, you can ask the school you would otherwise commit to if you can have a few extra days extension in light of the situation.

Congrats on your options! Very exciting! I seriously doubt anyone will care if your degree is an MS or an MA. If you are concerned about taking on PhD coursework while writing a thesis at Berkeley, I don't imagine that the experience will be different balancing PhD coursework while doing research at Stanford. In both locations it might be challenging as a masters student to find faculty willing to take you on; current or graduated students would be able to tell you more, or you could try reaching out to faculty you are interested in. I imagine it's a bit easier to get involved in research at Stanford, and while the thesis at Berkeley does have a concrete form, I wouldn't underestimate the hurdle of finding an advisor. Also, I think I saw someone on this board did a Berkeley masters with the intention of doing a PhD, not sure if they still hang around here. At Berkeley, many masters students do the third semester and take on additional coursework, usually electives and PhD level courses. Poking through the course catalogs at both Berkeley and Stanford, I honestly don't see too big of a difference between course offerings at both departments, topically speaking. Stanford will always have more courses/topics/titles since it's on the quarter system, so some things that are two quarter courses at Stanford might be merged into one long semester course at Berkeley. Honestly it's a bit easier to learn a bit about everything on the quarter system since everything goes fast! I think Berkeley's offerings might be a bit obscured since many special topics courses don't have dedicated course numbers, or are in the school of public health/CS/Econ without cross-listing in the stat department, whereas Stanford's offerings are mostly labeled as statistics. Basically, you might have to work a bit harder to find the course you're looking for at Berkeley since the labels are bad. Another point to consider is that the connection between Berkeley Stat and EECS is very strong, many faculty members have joint appointments. These connections could possibly make it easier to pivot from Berkeley Stats to Berkeley EECS for a PhD if you have the right advisor. Possibly- I don't actually know of anyone doing this for EECS (only biostat), but it seems feasible. There could totally be these connections at Stanford too, but it's a bit harder to tell since they don't have a public alumni page for masters students. For both places it seems uncommon for masters students to continue to the PhD. More common at Stanford, but it looks like these are exclusively Stanford undergrads who obtained a concurrent or co-terminal masters degree.

I think two of their current first years are Hispanic. Not that it makes the department diverse, but it appears to be a change from previous years.

Richard Li

Some professors at Berkeley also have ties to MIT through FODSI which has been very active this year. I imagine these ties will get stronger over time with FODSI postdocs at Berkeley. This tie is probably not as strong as Harvard, but it's worth noting that it is there.

Offers from Duke, Chicago, Berkeley, and Cornell were between 24-28k for 9 months with a 6k summer stipend, but UNC offered 18k for 9 months. I'll second the point about student fees. Another thing to think about is student health insurance, if you need it. Some schools cover all of it whereas some only cover a portion. The stipend can vary from month to month, however, depending on how you are funded, what your role is that semester (TA vs RA), and if there's been a break or not.

Somehow I ended up with "statistics" to "statistic's" in my submitted personal statements and I don't think anyone noticed. As long as it's not something like a different school name or a whole bunch of grammatical mistakes, I wouldn't worry about it too much.

If OP is a stat major, they had to have at least taken Math 300, which is an intro to proofs class. Last I recalled there were two quarters of Real Analysis required for the major (Math 327 and 424), but they did some restructuring in the past year. I vaguely remember an email about letting students swap the second class for more linear algebra, but it looks like OP hasn't taken that either? You're right. Thinking about it more, my money is actually on this applied math degree (https://acms.washington.edu/data-sciences-and-statistics), not the Statistics: Data Science major, which would explain why they had taken no real analysis. If that's the case, OP would have some more options, like taking I think the Optimization sequence (407) or Numerical Analysis (464), neither of which require the proofs class. Although, right now OP is out of luck since these classes are full and tend not to reduce in size. I think OPs best bet is probably to take second year of the honors calc sequence, 33X, since it's basically intense analysis, but that won't help with admissions this year. All measure theory classes are only offered in Spring quarter, which would give OP two quarters to improve their background. It's common for the honors calc students to take (or try to take, there's never a guarantee they will let you in) the various measure theory classes in Spring.

The issue is at UW it is borderline impossible to get into upper-division math courses without being a math major, and graduate math courses are also seemingly off the table for most undergrads, except the ones who take the honors sequence. At minimum, you need to take Math 327 (was this not a requirement for the major?), then Math 424 (analysis II) and Math 425 (metric spaces) in Spring. These are about the only math courses available to you as a stat major, besides 491 (stochastic processes) which you don't have the background for right now. If you really wanted to push yourself, you could try to take the grad measure theory for stats in Spring since you really only need the material in 424/a bit of 425, but I don't recommend it. An alternative would be to take the advanced calculus sequence, 33X, which will rigorous and very hard but it will give you a far better grounding in analysis than the regular courses and will also open doors for other opportunities in the math department. For instance, I think 33X has some special algebra topics session that you could get involved in. Chatting with the stat undergraduate coordinator (not the major advisor) is a good idea. Talk to her about your plans if you haven't already. She'll be able to give you more personalized advice.

Super late to the party Undergrad Institution: Public flagship, known for stats Major(s): Statistics and Mathematics Minor(s): Spanish GPA: 3.8 Type of Student: Domestic White Female GRE General Test: taken cold Q: 166 (89%) V: 164 (94%) W: 4.5 (57%) GRE Subject Test in Mathematics: M: AWFUL, only submitted to Stanford Programs Applying: Statistics PhD Research Experience: Assorted quarter-long projects + presentations not super related to stats during freshman/sophomore year Summer fellowship doing mathematical modeling at government institute, no publications REU in topological data analysis, conference poster, helping teach a related short course in fall Two years on an applied Bayesian project, first author on manuscript, conference poster Applied capstone project, conference poster Senior honors thesis in causal inference Awards/Honors/Recognitions: Phi Beta Kappa, Dean’s List, a silly award for “best teamwork” at a data hack but I’m proud of it, some travel awards if that means anything, and a university system wide award (all campuses) for outstanding students Pertinent Activities: Statistics tutor for 2 years, grader for some intro stat courses, statistics club president (a ton of diversity outreach), director of a statistics directed reading program that I founded to try to encourage more diversity in the major, algebraic statistics reading group Related Work Experience: None. Working at a call center sucks, don’t do it. Letters of Recommendation: One lecturer I took a class from, also my boss at the tutor center. Fairly well known in statistical education. We’re quite close and they wrote a strong letter, likely emphasizing creativity/character. Lecturer I wrote my applied Bayesian paper with. A very well-known professor in the department who I took a class from but have no research involvement with. While I think my letters were all strong, I really should have asked my thesis advisor or even my REU advisor to write me a letter. In hindsight, I think omission of these letters was a bad idea and maybe appeared to reflect poorly on my research abilities. I was worried about the weight of a letter from a well-known professor who I didn’t do research with versus a less-known, more junior professor who I did work with. I wasn’t sure how to strike a balance between these things. I think I gave the admissions committees a lot of insight into my character and my love of statistics, but less into my research abilities and potential which is likely what admissions cares about. Math/Statistics Grades: Math: Calc I, II, III (A, A, B), Multivariable Calc (A-), Intro to Proofs (A), Diff Eq (A), Linear Algebra (A), Linear Analysis (A-), Intro Real Analysis I, II, III(A, A-, A-), Abstract Linear Algebra (B+), Probability I, II, III (A, A, A-), Rings (A), Groups (A), Differential Geometry I, II (B, A), Topology (A), Metric Spaces (A-), Measure Theory (A-), Stochastic Processes (A) Stats: Math Stats II, III (A-, A-), Experimental Design (A-), Applied Regression (A), Resampling (A), Non-Parametric Statistics (A), Machine Learning (A), Statistical Thinking (A), Grad Math Stats I, II (A-, A), Grad Stochastic Processes (B+), Grad Bayesian Statistics (A), Grad Network Statistics (A), Grad Linear Models (A), Grad Survey Sampling (A) Other: Java I, II (A, A-), Scientific Computing (B+), Biological Modeling (A), Grad Stat Computing (A) Results: Berkeley – Statistics PhD/ Rejected / Admitted (oops?) Chicago – Statistics PhD / Admitted Duke – Statistics PhD / Admitted Cornell – Statistics PhD / Admitted UNC – Statistics PhD / Waitlisted / Admitted Wisconsin – Statistics PhD / Waitlisted / Withdrew Stanford – Statistics PhD / Rejected (a waste of money in light of Math subject GRE score!) CMU – Statistics PhD / Rejected Harvard – Statistics PhD / Rejected Columbia – Statistics PhD / Rejected Yale – Statistics PhD / Rejected UT Austin – Statistics PhD / Rejected Misc: On a sort of interesting note, three women with similar profiles at my school all applied to roughly the same set of programs. One stronger (+academic legacy), and one more econ focused. I was told by one professor that some schools admit only the top 3-5 women, so the same few women are admitted to all schools and the cohorts end up wildly unbalanced. I’d be interested in knowing if/how this came into play, especially since we are all from the same institution. Anyway, if you’re reading these profiles and feeling generally bad (like I did!), you’re better than you think you are and I believe in you. You have value beyond your research experiences and admission results. Be kind to yourself- it's not worth going super hard and burning out.