galois

It seems to vary widely across programs. I just used particular school searches and sorted by decision date in the results section, e.g. like this For the ones I applied to, most are much earlier than mid march, and typically make first rounds by mid-January / early-February. But of course, I don't know how accurate those results are.. maybe someone else can chime in with what to expect from a given school, if you have a top choice in mind.

Agreed, I think this went out to everyone. And I'm glad it did because the ladder faculty page has been updated since I submitted my SOP (and they asked you to list 3 in the SOP). Looks like a professor was recently hired who is specializing in exactly my area of interest, so that's awesome. I'm curious though, is it considered risky to pursue an advisor who has just recently joined the faculty, and thus has no history of advising students?

It would appear that this is a very new department; given that the current students page only goes back to 2016, I would guess there have been no graduates yet. Does anyone have an opinion on the program, based on its faculty or academic/industry placement potential? Is it risky to attend such a new department? Of course, UCSD is renowned when it comes to Math and CS, of which there is some overlapping faculty, but is notably missing from the conversation here in Stat land.

When you refer to UNC having a theoretical focus, is that strictly for the Statistics degree? I was also considering the Statistics & OR combined degree, but after comparing the curriculum difference, I'm concerned the latter will have less theory and be more applied. EDIT: Just realized you were referring to the Biostat degree. Thanks for tip. Do you have an opinion on the Stat/OR degrees though? I have mixed feelings about this. Everyone on this forum, and elsewhere on the internet, is of the opinion that they are pretty much the same degrees. But, while my interests in statistics itself is rather broad (high dimensional data analysis/inference, ML, topological perspectives, probably more that I'm not aware of yet), I am picky in my applications. For whatever reason, I'm not particularly interested or inspired by biology as a science. I am aiming to apply research in the area of environmental science, ideally climate/weather forecasting, possibly another physical science like astronomy. I would also be interested in sociology applications, specifically regarding economic inequality, effects of public policy on such problems, etc. So, to that end, most of the programs I have picked have at least some faculty applying research to those domains. What do you think? Are there flaws in that thought process?

OK, thanks for your help @bayessays. I think my list is final. It has actually changed quite a bit in the last week or two, influenced by your comments and further research. U Washington - Statistics Caltech - Computing & Mathematical Sciences Duke - Statistics UNC Chapel Hill - Statistics & Operations Research NC State - Statistics UCLA - Statistics UT Austin - Statistics U Florida - Statistics UC Irvine - Statistics College of Charleston - Mathematical Sciences with Statistics Concentration, MS I feel good about this for the first time since starting the search a few months ago. Thanks again.

I'm curious if it helps to include a class rank in your application for those schools with harsher grading, since that would be a tad more objective. Perhaps in your CV you can list rank percentile or something (typically you can call your registrar for this information). Just a thought, not sure if this is typical or how closely CVs even get read during admissions.

Thanks for the advice. It has been tricky finding schools with such strict geographic requirements, most of which are imposed by my fiance - she really hates the cold. But, since we're moving solely for my education, it's only fair. It's a shame so many of the good programs have such insane costs of living though. I'm afraid of actually getting into Berkeley and then having to figure out how to afford it?. I did have UCLA and NCSU on my list at one point. I read some fairly damning things about the UCLA department on this forum, but I suppose those were just anecdotes. The reason I took NCSU off the list was mainly its size. I like a smaller student-faculty ratio. But if I'm shuffling things around, removing some of those UC schools, I should reconsider it.

Undergrad Institution: Top 30 Liberal Arts College Major: Mathematics Concentration: Scientific Computing GPA: 3.91 Class Rank: Top 4% Type of Student: Domestic Mixed-Race Male Relevant Courses: Calculus I-III (A), Intro Statistics (A), Random Structures (B+), Foundations (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), Euclidean & Non-Euclidean Geometry (A), Intro to Programming (A), Data Structures & Program Design (A), Complex Systems in Scientific Computing (A), Scientific Computing Seminar in Parallel Computing (A), Independent Study in Computational Algebraic Coding Theory (A) GRE (V - Q - W): 167 - 168 - 4.0 (98% - 94% - 59%) GRE Subject Math: (taking 9/15 for the first time) (Note: I'm cramming now, but given the time since undergrad, this subject score will likely be horrendous. Only taking for UCSD / Yale.) Research Experience: - REU in combinatorial mathematics: presented at JMM, won 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. Teaching Experience: - Lead tutor for Calculus I-III for a few years, also helped in class with Maple syntax and stuff. - Tutored an autistic client over a summer and increased his placement exam scores so that he could attend the local community college program. Coding Experience: I've been in software development 4 years. Standard web technologies, passable Linux sys-admin skills, experience in PHP, Python, Javascript, Bash, etc., and more recently functional programming in Haskell. Did some C, C++, Maple, Mathematica, Matlab back in college as well. Recommendation Letters: Although its been 5 years, I made a great impression on my profs, and they were very happy to hear that I've finally decided to go back to school. I think these letters will be very strong. All 3 from Math department. Programs Applying: Mostly Statistics PhD. Research Interests: I'd like to be able to stay within the realm of the software industry, but move away from computer science engineering problems and get closer to mathematical problem solving, ideally in a research role. I think the big data trends have opened up these sorts of opportunities in the form of research scientists dealing with ML / computational statistics / etc. in industry. That's kinda my main motivation for pursuing graduate school, so I'd like to have that kind of computational focus, high dimensional data, etc. Schools of Interest (Statistics Degree unless otherwise noted): UC Berkeley Caltech (Computing & Mathematical Sciences) Carnegie Mellon (Statistics with ML Joint degree) University of Washington Duke UNC Chapel Hill (Statistics & Operations Research) UC San Diego (Mathematics with a Specialization in Statistics) Yale UC Davis USC Marshall School of Business UT Austin UC Irvine UC Santa Barbara (Statistics & Applied Probability) I think I've finalized this list of schools. But honestly I have no idea if I'm off base, even after looking at everyone else's profiles. Am I throwing money away by applying to the top tier like UCB, UW, CMU, etc.? If you have suggestions for adding/removing programs, please comment! Would love to save money on applications. My main criteria: 1) temperate climate, 2) affordable living, 3) strong theoretical coursework 4) alignment with research interests described above, and 5) small-ish departments. In addition to recommendations, does anyone have experience with the programs at UCSD or UC Irvine? UCSD is notably missing from Stats ranking but its Math and CS programs are very well regarded. Does anyone know what the stats specialization is like? Also, the Irvine department website seems half developed, so it's hard to get a feel for that department.

I doubt you have time to read a separate book during your first year, but I recently purchased some Dover books to brush up on material before entering grad school next year (hopefully ?). I bought Linear Algebra written by George Shilov, translated by Richard Silverman specifically for its axiomatic, proof-heavy feel. It's dense and definitely not a quick read, but as someone who has already seen Linear Algebra before, I think it was a good choice. It's possible that you might be able to dissect it and just read the areas where you need brushing up, but I'm not positive; the layout of the book is definitely different than the curriculum I was taught in undergrad. For example, the first chapter is on determinants.. it seems like an odd choice to me, but I imagine I will appreciate it in time.

Interesting thoughts from both of you, and a good things to keep in mind. Thanks.

Okay, this makes me feel better. And yeah, I suppose it's not as weird of a question if I've already been accepted and am just trying to have more information. Thanks for responding!

Thanks, that's very reassuring. I do worry about the programs with verbiage such as "exceptional cases" and if those ones might make you jump through hoops.

@Sigaba Thanks for the help. That document is specifically for Department of Mathematics, and other departments, like Department of Statistics, might not follow the same policy.

I think it's an important to have an exit strategy in the case that a doctoral endeavor goes sideways, whether that be financial problems, marriage problems, or failed candidacy. Hence, part of my decision process on schools is whether or not there is a viable exit strategy. Ideally, an M.S or M.A. is typically awarded on the way to Ph.D. Next best option is that it is typical to be awarded an M.A. upon failure to advance to candidacy, provided that masters requirements are satisfied. Every other school I've encountered either 1) says nothing about the topic or 2) says that masters might be awarded in exceptional cases. I've had to really dig through handbooks and alumni listings to figure this out for most schools, but still for some I could not find any information. Can anyone help me complete this list, or tell me a good way to figure this out? Somehow I think emailing and asking sends the wrong message.. School (Stat Dept unless specified) - Masters awarded UC Santa Cruz - Yes U Washington (Applied Math Dept) - Yes Caltech - Maybe (exceptional cases) Duke - Maybe UC San Diego (Math Dept) - Maybe UC Davis - Maybe UC Irvine - Maybe UC Santa Barbara - Maybe UC Berkeley - Unknown U Washington (Stat Dept) - Unknown NC State - Unknown UNC Chapel Hill - Unknown University of Florida - Unknown USC - Unknown UT Austin - Unknown

I'm curious what other people think about this as well, as I might end up in a similar case. My answer would be that it depends on the program though; specifically, if you look at the coursework for each masters program, I'm sure you'll find some that are 1) more applied 2) more theoretical and 3) evenly balanced.

I thought I'd be able to keep editing my original post as profile came to be, but I guess there's a time limit. Undergrad Institution: Top 30 Liberal Arts College GRE Raw: V 167 - Q 168 Felt like I earned at least a 4 on writing as well. Is that good enough for top programs? Can't decide if it's worth taking again. I basically just want a solid program in a nice setting (read: surf available) where the living stipend is actually liveable. I'm starting to think most of the UC school stipends are simply not enough to avoid debt.

I'm sure we're all familiar with the countless tales of PhD depression, drop out, mental health issues, etc. caused by entering into a PhD for the wrong reasons, whether that be for job prospects, social status, or what have you, instead of the correct reason: passionate desire to produce novel research in a particular field. Do you guys think this applies to the field of Statistics? I ask for myself, and also because I notice many posts relaying the same thoughts that I have: indecision between MS and PhD due to cost of MS. If I'm honest with myself, if MS programs were typically funded, I would much rather commit myself to just two years and then see how I feel afterwards. Does this mean I should reevaluate applying to PhD programs?

Undergrad Institution: Small Liberal Arts College, Good but not well known school Major: Mathematics Concentration: Scientific Computing GPA: 3.91 Graduated: 2013 Awards: summa cum laude, Phi Beta Kappa, Pi Mu Epsilon National Mathematics Honor Society, a number of college-specific prizes and scholarhips, JMM presentation prize Type of Student: Domestic Mixed-Race Male Relevant Courses: Calculus I-III (A), Intro Statistics (A), Random Structures (B+), Foundations (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), Euclidean & Non-Euclidean Geometry (A), Intro to Programming (A), Data Structures & Program Design (A), Complex Systems in Scientific Computing (A), Scientific Computing Seminar in Parallel Computing (A), Independent Study in Computational Algebraic Coding Theory (A) GRE: (taking 8/11 for the first time) GRE Subject Math: (taking 9/15 for the first time) (Note: I'm cramming now, but given the time since undergrad, this subject score will likely be horrendous and I'll only send it if required) Programs Applying: Math/Applied-Math/Statistics MS/PhD (very undecided!). Probably unwilling to pay for MS, so ideally PhD. Research Experience: - REU in combinatorial mathematics: presented at JMM, won 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. Teaching Experience: - Lead tutor for Calculus I-III for a few years, also helped in class with Maple syntax and stuff. - Tutored an autistic client over a summer and increased his placement exam scores so that he could attend the local community college program. Recommendation Letters: although its been 5 years, I made a great impression on my profs, and they were very happy to hear that I've finally decided to go back to school. I think these letters will be very strong. Coding Experience: I've been in software development for over 4 years now. Standard web technologies, passable Linux sys-admin skills, experience in PHP, Javascript, Bash, etc., and more recently functional programming in Haskell. Did some C, C++, Maple, Mathematica, Matlab back in college as well. Research Interests: not sure. Simultaneously interested in CS - programming language theory, Math - general applied math, also lately considering Stat with the goal of moving towards data science. I'd love to solicit some advice. I do enjoy software development, but I'm not sure it will entertain me for an entire career. I've realized that I need to go back to school, if only because I've always wanted to study more math. My academic interests are in pure math, but I don't think I want to be in academia. Based on my latest foray into FP, I do think I would enjoy PLT, but I'm not sure I would enjoy general Computer Science, let alone 5-6 years of it. Furthermore, in the end I'd be looking for a research position in PLT and that would be very competitive. So, I started to look at more general applied math. I figure I have mathematical talent that I should be using to help the world (which IMO has plenty of software devs), so I should study some applied math that could be used in more general situations, perhaps climatology research or helping elect sane politicians, etc. In balancing enjoyment of the degree and the career prospects afterwards (I've come to appreciate a higher-ish salary and remote work), lately I've been considering Statistics and moving into Data Science. My current background would probably complement that career path well. Can anyone offer me advice? Specifically, what do you think about the degrees/careers I'm considering? Which schools do you think match my application profile? Should I be worried about having a 5 year break in studies? Given that I'd only be studying statistics as a means toward an industry career, and how I stayed away from stats in undergrad, how can I make my application look good for a PhD? Schools of Interest: University of Washington: Statistics/Applied-Math PhD UT Austin: Statistics PhD Duke: Applied-Math/Statistics - PhD UNC Chapel Hill: Statistics/Operations Research - PhD UC San Diego: Math (specialization in Stat) - PhD UC Santa Barbara: Statistics & Applied Probability - PhD UC Santa Cruz: Applied Math & Statistics - PhD UC Irvine: Statistics PhD U Hawaii Manoa: Math Phd College of Charleston: Math/Statistics - MS If you can't tell, location is a big deal for me. Fiance is unwilling to live anywhere cold, and I also love to surf, hence a lot of west coast schools. I'm local to Charleston, so if I don't get in anywhere, I'll probably do an MS there. Any suggestions on what I should add/remove to this list? Are any way out of reach for me, and are there any that I haven't thought of?