trynagetby

Undergrad Institution: Low-ranked, medium-sized public university known for engineering
Majors: Mathematics, philosophy
Minor: Applied statistics
GPA: 4.00
Type of Student: Domestic white male
GRE General Test:
Q: 169 (94%)
V: 166 (97%)
W: 6.0 (99%)
GRE Subject Test in Mathematics:
M: N/A
 
Programs Applying: Statistics and mathematics PhDs

Research Experience: I attended an REU in coding theory. From this project, I got an authorship on a paper. I also attended a well known SIBS program and have done some statistics consulting for my campus writing center.
Awards/Honors/Recognitions: I received a few department awards for "excellence in mathematics" and that sort of thing, and I've been a part of a few winning teams in regional math competitions. I also won a few awards for writing projects and have a pretty solid record of activities outside of math and stats.
Pertinent Activities or Jobs: I have minimal experience as a TA for calculus ii, but I have a few years of experience tutoring everything from math and stats to history and writing.
Letters of Recommendation: All of my recommendations came from professors in my department, none of whom are famous per se but each of whom knows me well.
Math/Statistics Grades:
Abstract Linear Algebra (A+) Group and Ring Theory (A+) Commutative Algebra (A) Algebraic Geometry (In progress) Advanced Calculus I (A+) Advanced Calculus II (In progress) Measure Theory (A+) Mathematical Statistics (A+) Introduction to Probability (A) Financial Mathematics (A) Design and Analysis of Experiments (A+) Regression Analysis (A+) Introduction to Applied Statistics I / II (A+ / A+) Introduction to Topology (A) Algebraic Topology (In progress) Dynamical Systems (A) Partial Differential Equations (A) Any Miscellaneous Points that Might Help: I have a pretty long list of various activities that each on their own aren't special but, when united together, form a CV so mean it makes medicine sick. (I just have a lot of extracurricular stuff. It's like I went to high school for college.)
Applying to Where: (Color use here is welcome)
Boston University (Math) / Rejected Michigan State University (Math) / Accepted (Declined) Northwestern University (Math) / Rejected Pennsylvania State University (Math) / Accepted (Declined) University of Illinois - Chicago (Math) / Waitlisted (Declined) University of Maryland - College Park (Math) / Pending University of Michigan - Ann Arbor (Math) / Pending University of Wisconsin - Madison (Math) / Rejected Carnegie Mellon University (Stat) / Accepted Columbia University (Stat) / Rejected Duke University (Stat) / Accepted North Carolina State University (Stat) / Accepted (Declined) Ohio State University (Stat) / Accepted (Declined) University of Chicago (Stat) / Accepted University of Michigan - Ann Arbor (Stat) / Pending University of Minnesota - Twin Cities (Stat) / Pending University of North Carolina - Chapel Hill (Stat) / Accepted (Declined) University of Wisconsin - Madison (Stat) / Accepted Reflection: I was quite anxious about my applications because I felt that my background was not brimming with research and gleaming with prestige, and I made two mistakes as soon as I finalized my list of options. Firstly, I should have been more assertive with my picks of stats programs. I didn't need to apply to every good school that I thought might accept me, and I could have reached just a little higher just for the sake it. Don't get me wrong - I'm extremely pleased with my outcomes and grateful, too. I did not expect to get accepted to Duke or CMU or UChicago and am still limited in my imagination to dream at this level. I probably could have passed on NCSU and UMinnesota, which are not as good fits for me, and instead thrown my hat in at Harvard or Stanford literally for no other reason than to see if it might've been possible. I probably wouldn't have chosen them if I did get accepted I suppose, but I admit that I do wonder. Either way, though, I realize that it would have been totally okay to take a gap year if I didn't get any acceptances for the reason that I shot too high and didn't play safely, and it would have saved me a few hundred dollars too. 
Secondly, I should not have gotten too excited about math programs - there's nothing wrong with math, but it's obvious to me now that I am supposed to devote my life to studying statistics. I hadn't spent a whole lot of time doing statistics, and the bells and whistles of Pure Math were always tempting me; any time I browsed YouTube or went to a math competition or attended Math Club, that spark of intellectual curiosity inside of me would jump a bit higher. I kept convincing myself that I only was considering statistics as a back-up because it's more profitable or less competitive. Somehow stats were an abhorrence, a perversion, a delinquency, and only were disguised as "a real job" in order to woo weary sailors away from that Ithaca in the ethereal Arts & Science College up above. It took some discussions with my professors and with my peers to climb down from that notion. Now I am more aware of what I want to do with my PhD, writing my statement of purpose became easier for statistics programs than for math ones.
I still feel very strangely about my results, honestly. From my perspective, this is the first time in my life that I've felt thoroughly verified for something that I've cared about. I'm not especially clever nor do I have outstanding achievements. I don't attend a prestigious university. I'm like a gritty country boy with a bit of a personality and some facility with math. What helped me stand out, then? If I were to guess why my applications to statistics programs were so successful, I would say that I presented a clear and honest sense of what my goals are and why I'm applying to grad school in my statements. I think of myself as a writer rather than a statistician, and I want to train myself to be the best darn science writer I can be. I think statistics is a deeply philosophical endeavor full of challenges for writers, but it also notoriously invites opaque reasoning when efficiency is prioritized over rigor. This problem, I feel, invites people like me whose competencies conspire to address it through good expository writing, and that is the main reason why I find the subject attractive. Meanwhile, I have some technical skills and want to continue learning and studying interesting problems. I didn't pretend that I am taken by unclarifiable passions for machine learning or statistical genetics, which I don't accuse *you* of doing, but I found it hard to interrogate myself to the point that I could actually say more than that I have similar passions. The specificity and authenticity of my motivation was probably the most affecting part of my application.
Toward this aspect of the journey, then, I would advise readers like me who come from more-or-less humble backgrounds to think seriously about what it is that you contribute. Prestige schools are like carnival games; it's worth the price to play. Just remember that in the academic elite people need to know who you are. You don't want to be Charlie Bucket who stumbled upon the last Golden Ticket and found his shoes on the ceiling by accident. I'm facing the fact that I will never feel like the smartest person in the room again, and I'm okay with that (although it's kind of spooky to think that there's going to be someone with a high IQ hiding under my bed). I'm excited to contribute my own experiences and ideas to whichever department I choose, and I have to know that I have that or else I won't be able to function when I get there.
Not to legislate on the exception to a rule, but considering my exceptional (meaning atypical, not superior) case, I would like to conclude that there is no "correct" way for one to improve one's applicant profile. As they say, there are many paths up the mountain, but the view from the top is the same. I may not know the first thing about mountaineering, but I think it's a bit like climbing the academic ladder. There are a certain number of cliffs or steep inclines that you will find yourself facing when you choose the Road Less Traveled. You can probably find the right equipment, but you need to be a little lucky to find a good deal or else it's going to be extremely resource expensive. Actually, I don't know how to bring this analogy together.
I hope my reflections have been entertaining if not insightful as well as inspiring without being pretentious.

Undergrad Institution: Ivy League
Major(s): Mathematics-Statistics
Minor(s):
GPA: 3.8999
Type of Student: Domestic Asian

GRE General Test:
Q: 169
V: 164
W: 5.5
GRE Subject Test in Mathematics:
M: NA

TOEFL Score: NA
Grad Institution: NA
Concentration:  NA
GPA: NA
Programs Applying: PhD: Statistics, Biostatistics, ML
Research Experience: Freshman research in social work examining social media data,  publication with  name far back, Sophomore Biostatistics REU: First author paper at PSB, 2 year of research in statistical Neuroscience at home Institutio, publication with name far back.
Awards/Honors/Recognitions:  Nothing really
Pertinent Activities or Jobs:  Tutor/TA
Letters of Recommendation: 1 from Assistant Professor in Linear Algebra Class I did well in, probably not well known in stats (just a did well in class rec probably, but I was pretty interested in his research and we talked a bit). 1 recommendation from Sophomore REU prof. Probably extremely good, but he's a prof at a pretty unknown school. 1 recommendation from statistical neuroscience prof. He's extremely well known but I didn't do too hot in his lab so probably rec was just "this kid is persistent and can grind".
Math/Statistics Grades:
Bunch of lower division classes (A).
Real Analysis I/II - A
Abstract Algebra I  - B+
Fourier Analysis - B
Measure Theoretic Probability - A-
Numerical Analysis - A+
Analysis of Algorithms - A
Artificial Intelligence - A
Statistical Machine Learning - A-
Bayesian Statistics - A
Statistical Inference -A
Causal Inference (CS) - P (covid)
Abstract Linear Algebra - P (covid)

Applying to Where: (Color use here is welcome)
Stats:
School - University of Michigan
School - University of Washington
School - Duke
School - CMU
School - University of Washington
School - Wisconin-Madison
School - University of Texas Austin (waitlisted- then accepted)
School - UCLA (ghosted probably rejected)
School - UNC (ghosted probably rejected)
School - Cornell (ghosted probably rejected)
School - Rice
Biostats:
School - University of Washington
School - Harvard
Misc:
School - MIT (SES)
School - Georgia Tech ISYE (ML Program)
School - Northwestern IEMS
Advice for Posterity and Reflection:
Overall I'm pretty happy with my results and I'm pretty excited to be attending Duke. One caveat is that I severely underestimated my application (thought I would get into maybe one of UWashington, Duke, Mich). I didn't think I had a chance at Harvard/Berkeley  because of my so-so performancein upper division math classes and lack of graduate coursework , I didn't apply. My rec writers put a cap on schools beforehand (totally reasnoble given my paranoia) so I overspent my school allowance on safety schools. So future applicants please take risks because the regret is real. Tbh I probably wouldn't have gotten in to Harvard/Berkely but now I'll never know.
Some general advice
- all 3 of your letters don't have to be crazy strong. I think I only had 1 really strong letter that sang about my potential as a top researcher. While he was from a pretty unknown school he had sent quite a lot of students to top programs so that might have helped. The other letter from the neuroscience prof I did research for probably wasnt that great as I wasn't stellar and probably one of his worse students (but he has pretty good students). this letter probably said I was competent, hardworking and easy to work with. My last letter likely just spoke to my interest in math and that I was mathematically competent. So not crazy strong letters.
- undergrad school prestige matters a lot. So if you're from a top grade deflated school and have bad grades still shoot for the top! Hopefully my ehh profile encourages ya'll.
- Harvard/UWashington Biostatistics are not safety schools (typing this sentence, it seems obvious). I thought Biostatistics programs are much less competetive than statistics. But obviously that's not really the case as I got rejected by UWashington biostat and into UWashington Stat. Also I went to Harvard Interview day and people were super accomplished even before getting cut. I'd put those tier of biostat programs on the same competitiveness as UMich-Duke-UWashington Stats (probably difference that they place more emphasis on undergrad research and a little less on math).
- That being said if you're interested in Stat seriously consider top Biostat programs they do super cool work and I feel like I would have been very happy at Harvard if I didn't certain offers I did.
- Don't take math classes you're not interested in just to have more math classes. That's how my B in fourier analysis happened.
- Operations research programs are much less competetive (outside like MIT/Berkely) if you're interested in applied stats stuff.
- Hot take: I disagree with the prevailing wisdom that the SOP doesn't matter. I think Recs + Grades are much more important but with so many people applying I think that demonstrating that you can coherently express your research experience and that you know what type of work academic statisticians do via your research interests can move your application from the "consideration pile" to the "accept" pile.

I found the applicant profiles and admission results over previous years to be helpful while selecting the schools I chose to apply to, and now seems like a good time to start the thread for this year. Copy the template below and fill in as much info as you would like. Keep in mind that you can't edit your post for very long after posting, so it may be good to wait until you have most of your results before posting.   Here are some links to threads from previous years: 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020.   Below is the template: Undergrad Institution: (School or type of school (such as Big state/Lib Arts/Ivy/Technical/Foreign (Country?))
Major(s):
Minor(s):
GPA: Type of Student: (Domestic/International (Country?), Male/Female?, Minority?)

GRE General Test:
Q: xxx (xx%)
V: xxx (xx%)
W: x.x (xx%)
GRE Subject Test in Mathematics:
M: xxx (xx%)

TOEFL Score: (xx = Rxx/Lxx/Sxx/Wxx) (if applicable)

Grad Institution: (school or type of school?) (if applicable) Concentration: 
GPA:
  Programs Applying: (Statistics/Operation Research/Biostatistics/Financial Math/etc.)   Research Experience: (At your school or elsewhere? What field? How much time? Any publications or conference talks etc...)
Awards/Honors/Recognitions: (Within your school or outside?)
Pertinent Activities or Jobs: (Such as tutor, TA, etc...) Letters of Recommendation: (what kinds of professors? "well-known" in field? etc.) Math/Statistics Grades:  (calculus sequence,  mathematical statistics, probability,  real analysis etc.) 
Any Miscellaneous Points that Might Help: (Such as connections, grad classes, etc...)

Applying to Where: (Color use here is welcome)
School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined
School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined
School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined

If research at UW is better aligned with your interest, I'd vote for UW. You would also have much more choices at UW than Duke. As to academic placement, I think this is something more out of self-choice. Given the great location of UW and their prestigous department, it's not strange for most of their PhD graduates to be prone towards industry positions. But if you want to stay in academia, I believe UW is able to help you succeed just like what Duke could do.

These are all great replies of course and following the advice given in them will certainly benefit you. For me, reading Resnick's A Probability Path was helpful, but now after doing two semesters of probability, I prefer Durrett's or Billingsley's treatment of the material.
 I would also mention that once the program begins, you will likely be consumed by all the coursework, and I found it rewarding to look at some topics that I was interested in but that wouldn't be immediately relevant, knowing that I probably wouldn't have as much time to devote to them once the program began. For me, I took the time to learn more about combinatorics and topology, in particular. (Of course, such topics could become relevant in the context of research, but I investigated them more for my own enjoyment at that point than out of any desire to get ahead of the material.) 
As for Bayesian statistics itself, I am partial to Robert's The Bayesian Choice, which is written in an engaging style and covers a fair amount of ground in establishing the Bayesian paradigm.

Speaking specifically about Duke:
Stochastic processes (specifically applied to MCMC and HMMs and such) get covered as a big part of a second-semester class ("Probability and Statistical Models").  That said, how important stochastic processes are in that class varies by Prof -- if Mike West teaches it then you'll probably eat autoregressive models and stochastic processes for breakfast (and he's a wonderful teacher, so you'll enjoy it!), but if a different prof teaches that class then you might get a bit less stochastic processes and s bit more E-M alg, finite state space MC, and so on.  My point being, what you need to know (at least for the qualifying exam) will be taught, they're not just going to assume you've learned stochastic processes ahead of time.
Martingales are covered in the measure theory course most of the time, although since they're at the end they're liable to get abbreviated if stuff runs over.  Again, you'll learn what you need, at least for quals.  Real analysis is definitely helpful in prepping for the measure theory course, though.  I will note that it might be possible for you to skip measure theory and the intro Bayesian course (mentioned below) if you have strong background there already, although I think that's less likely (/impossible?) to be the case in the future than it has been in the past.  
Duke has you take a Bayesian class first semester (this is the undergrad/master version, but an OK idea of the topics anyways), but even so it's nice to have basic background in conjugate prior sorts of models and coding basic Gibbs samplers by hand (you know what I mean, no JAGS or Stan).  If you have that you're likely fine, although the intro Bayesian class is switching from combined MS/PhD to having a PhD-specific class that may cover more advanced topics going forward.
I'd definitely recommend going over Casella & Berger if you're rusty, since the inference class (which is second semester) is probably the most important class for quals and is taught from TPE2, so there's some assumption you're familiar with C&B and maybe CMT and Slutsky's theorem that aren't covered all that much in the inference class (or they come up right at the end) but that will be relevant on the quals + potentially going forward.  
Code-wise, it's mostly R in classes, although some profs prefer MATLAB.

For topics to learn for research, Stat Asst. Prof's your person, as you already realized.

Before starting the PhD, I did review Calculus, probability and statistics (at the Casella/Berger level), real analysis, and linear algebra (including proofs). I think that this was helpful for the classes that I had to take in my first year, since it was fresh in my mind right before starting classes.  
I didn't study any measure theory, stochastic processes, functional analysis, etc. before starting the PhD. I don't think this is really necessary, but if you are very interested in it, it could potentially be useful... though I should note that by the time you start research, chances are you will forget most of this stuff. At that point, you can just relearn what you need for your research and fill in any gaps as you go.  But I do recommend reviewing some of the topics in my first paragraph because you'll be able to use that stuff right away when you start taking classes (rather than possibly needing it one or two years later when you start your dissertation research).

It's not a measure-theoretic treatment (such as a class in stochastic processes you might take after a first semester course out of a book like Billingsley), but Resnick's Adventures In Stochastic Processes is one of the most entertaining math books I have ever read - highly recommended.  @Stat Assistant Professor is much more of an expert, so defer to his opinion, but I don't think a huge knowledge of martingales will be needed; the only time I see martingales come up is in some people's theoretical work on Bayesian asymptotics.  Keep in mind that you won't really have to learn much of this stuff that you don't want to -- for instance, if you don't like martingales or functional analysis, most people at Duke will be doing research that is much more applied.  My personal advice is always to just focus on mastering the materials in your classes and if you are starting research, to learn that material.  Unless you really love doing the extra math, the effort:reward ratio is probably not worth it.

If you got A's in your graduate real analysis class + you have a good relationship with 2 of your letter writers you're in good shape. I'm fairly confident that just having a good relationship with your profs so they write LOR is more important than publishing.  Given my previous setence you have a strictly stronger profile than me and I was got into Umich/Duke/Wisconsin/Uwashington.  Unfortunately I didn't apply to Harvard/Berkley (regret it now lol). So I think your floor should be the schools mentioned above and apply to all the schools above.

So I do mostly research in the area of Bayesian statistics (though not exclusively), and I have done both applied and theoretical research in this area. 
I would say for theory: it is pretty important to know analysis and linear algebra well and to be comfortable with probability theory and stochastic processes. Unless you are doing very hardcore theoretical research (and there are some people who do that), you don't need to know measure theory that well, but you should be comfortable with it. Plus, measure theory/probability theory can be pretty useful for Bayesian nonparametrics. In Bayesian nonparametrics, you frequently replace finite-dimensional prior distributions with stochastic processes (e.g. Dirichlet process, Gaussian process, etc.), and it can be useful to know a little bit of measure theory and probability theory. 
For methodology/applications: obviously be familiar with the Bayesian paradigm, as well as MCMC (Gibbs sampling and Metropolis-Hastings) and maybe variational inference. Most of the time, the posteriors are intractable, so you do need to do approximate inference. It would be useful to be familiar with some of the "classical" models for linear regression, classification, and semiparametric/nonparametric methods for regression/classification. I think once you specialize in a particular research area (e.g. spatial statistics, functional analysis, topological data analysis, etc.), you can learn that stuff on your own. There's no need to study it prior to starting your research, unless you are very interested in it.  
For programming: Be proficient with programming in R and comfortable with using C/C++. Since R can be a bit slow and have a lot of overhead (compared to C/C++), you may prefer to code in C/C++ and integrate this code with R. R is great for creating plots and visualizations, etc., but if you are going to run MCMC (for example), you may prefer to use C/C++ and then integrate this with R, because your code will run a lot faster.

Pretty sure he means stanford is a tier above everyone else for stats.

Here's my very affective (read unscientific, subjective) rankings from  stalking alumni placements and professor productivity. It's pretty much a reranking of US News with the added information of the tiers indicating where the big jumps in quality are. I think within tiers the choice doesn't matter too much.
Stanford Tier:
Stanford.

Elite Top:
Berkley, Harvard, CMU, (Likely UChicago but I didn't research them)
Top:
Uwashington, Duke, Michigan, Columbia, Cornell, UNC
Up there:
NCSU , TAMU, UT Austin, UCLA, Wisconsin

Hi, I'm an U.S Asian male, so I hope I don't come off as mansplaining. Your situation definitely sounds very painful and maybe fits into larger problems of University administration. There seems to be a principle-agent problem when it comes to diversity. Most pressure to diversify comes from administration and maybe a few faculty members, but the day-to-day operations are left to your average professor 95% focused on research. So unless the majority of faculty  are super passionate,  departments can't really do much above the minimum to keep the administration happy (and how is the administration supposed to know that measure theoretic probability is super hard lol).
At the risk of sounding like all the other people you talked to, I hope you don't get too discouraged. I honestly don't see white women benefiting that much from AA at the graduate level. There are  a lot of fully qualified, exceptional women applying to Stat PhD programs and the admission statistics reflect this. Adcoms will not admit unqualified people no matter what the administration says because  ultimately its the department that is shelling out a lot of money for you.  You are probably qualified, maybe not overqualified, but qualified nonetheless.
Admissions are super random, and not completely based on visible factors. It's likely you really impressed your recommenders and they wrote killer things about your potential that are separate from your mathematical ability but are just as important.
On ways to raise concerns, maybe try the diversity dean/officer/secretary. They'll advocate for you without naming you and professors expect that type of stuff from them without looking into it.
Also to comment on your concerns that your peers look better than you on paper. I look like I have a a decent math profile from an Ivy league. But I'm actually not good at math/analysis, we just have a ridiculous amount of grade inflation.

The Feeling when acceptances/waitlists have come out for CMU and you still haven't heard back:

The Feeling when acceptances/waitlists have come out for CMU and you still haven't heard back:

The Feeling when acceptances/waitlists have come out for CMU and you still haven't heard back:

I got the final offer from the University of Iowa with TA. EXTREMELY HAPPYYYY!   

I don't think they're necessarily directly comparable, since Annals of Statistics pertains mainly to mathematical statistical theory (and is indeed the most prestigious stats journal for statistics theory). I would say among methodologists/theoreticians, Annals of Statistics is considered more prestigious.
 However, Annals of Applied Statistics is considered a top-tier journal and has had some very influential papers appear in it. For example, the original Bayesian additive regression trees (BART) paper (BART is one of the top-performing ML methods for prediction) and the pathwise coordinate optimization paper by Friedman et al. appeared in Annals of Applied Statistics. 

I'm doing the same for Statistics PhD programs. I can't recommend MIT open courseware enough for reviewing Real Analysis/learning more advanced probability theory. They have exercises, book chapters, and problem solutions. They even post exams with solutions so you can test yourself. It's really helpful with harder books like Rudin because they provide lecture notes where Rudin is lacking and points you to doable problems instead of the crazy impossible stuff. I'd also recommend reading/working through some more rigorous applied math books. Examples are like Linear Algebra by Friedberg, Insel, Spence and Applied Analysis by Hunter.

Is anyone else still waiting on CMU? I'm still holding out hope because I applied to one of the join programs and it seems like they historically might come out later than the core.

Hi, I'm an U.S Asian male, so I hope I don't come off as mansplaining. Your situation definitely sounds very painful and maybe fits into larger problems of University administration. There seems to be a principle-agent problem when it comes to diversity. Most pressure to diversify comes from administration and maybe a few faculty members, but the day-to-day operations are left to your average professor 95% focused on research. So unless the majority of faculty  are super passionate,  departments can't really do much above the minimum to keep the administration happy (and how is the administration supposed to know that measure theoretic probability is super hard lol).
At the risk of sounding like all the other people you talked to, I hope you don't get too discouraged. I honestly don't see white women benefiting that much from AA at the graduate level. There are  a lot of fully qualified, exceptional women applying to Stat PhD programs and the admission statistics reflect this. Adcoms will not admit unqualified people no matter what the administration says because  ultimately its the department that is shelling out a lot of money for you.  You are probably qualified, maybe not overqualified, but qualified nonetheless.
Admissions are super random, and not completely based on visible factors. It's likely you really impressed your recommenders and they wrote killer things about your potential that are separate from your mathematical ability but are just as important.
On ways to raise concerns, maybe try the diversity dean/officer/secretary. They'll advocate for you without naming you and professors expect that type of stuff from them without looking into it.
Also to comment on your concerns that your peers look better than you on paper. I look like I have a a decent math profile from an Ivy league. But I'm actually not good at math/analysis, we just have a ridiculous amount of grade inflation.

I think last year there were posts here about how disorganized UCLA's program was too.

I wouldn't worry about what previous alums do - even at top programs, a lot of people just go into data science roles because of the money.  Most people who go to lower programs don't want to be academics, so most of this relationship is not causal.  I'm not saying program reputation doesn't matter at all, but it matters much less than who you work with.  There are plenty of great professors at many programs ranked 25-50.  I understand if you don't want to post the program because of privacy concerns, but it is hard to give advice without knowing what your alternative is.  If it's a school like OSU/UIUC/UF/UCD/UCLA/UT/UCI (not exhaustive list) that's in the top 50, I'd consider the offer very seriously.

Thanks for the encouragement, @trynagetby
The program is lower than 25 (>25). And I haven't taken Real Analysis except to look up concepts for work. I've already enrolled in Cal summer school since the feedback, figuring it'll be good refresher before graduate program any ways. I got paranoid about the program because I saw most alumnus go on to DS work. I don't want to leave my DS job to work 5-6 years for a phd to be eligible for only more DS positions.  

Anyone else get an offer from Uwashington this week? I have a deep suspicion that I got off a hidden waitlist haha. Also to people waiting to hear back from schools that have already released acceptances: There is still hope!