What are the hardest stats & biostats programs?

[bernoulli_babe]

Which programs have grueling coursework for early years? I'd say Casella Berger is a solid read and manageable. Would Casella Berger be too easy for these programs?

I was looking through a few programs and these were my thoughts: 

Hard: 

Berkeley (Stats), Stanford, UChicago (Stats), Duke (Stats) , UNC (Bios) , CMU, UW (Bios)

Manageable: 

Harvard (stats + bios), Berkeley (Bios), Michigan (stats + bios)

[Stat Assistant Professor]

I don't think I would call any PhD program in Statistics particularly "easy." Many require a year of Casella & Berger, and the professors might make the exams very tricky. You have to practice a lot in order to learn how to be "clever" enough to pass the qualifying exams. That said, maybe somewhere like Stanford or UPenn Wharton would be considered "hard" because they start you out in measure theoretic probability and asymptotic theory your first year. But the students that they admit have typically already taken Casella & Berger-level statistics, linear models theory, etc. (and often, other advanced courses like stand-alone measure theory) before entering.

I don't think I would distinguish programs as "hard" vs. "manageable" vs. "easy." I would call some programs more "accelerated" or more "comprehensive" than anything else -- "accelerated" in the sense that what are second-year classes at most schools are first-year classes at these particular schools. And they may also be more "comprehensive" in that they might cover more material (e.g. some top biostat programs teach measure theory in their curriculum, but a lot don't). But it's not like students at other programs wouldn't be able to manage this coursework too if they were required to learn that stuff.

Edited September 17, 2020 by Stat Assistant Professor

[MathStat]

Great response by @Stat Assistant Professor. 

Casella Berger is already assumed knowledge for some top programs. But if you are admitted based on your pure math background (like yours truly) you likely won't have even cracked open Casella Berger or have taken a proper mathematical statistics course before coming in. However (and I hope this is not too strong of an opinion), Casella Berger presents math stat in a really outdated way. More modern and useful texts nowadays are from Van der Vaart, Lehmann and Casella, Iain Johnstone's Gaussian sequence model book, etc.. IMO Stanford does their math stat sequence in the best and most modern way. Their lectures and homeworks are online. The last part of their sequence, STAT 300C focuses on multiple testing, which is a very hot topic nowadays. 

Also, "hardness" of a program is a really subjective thing. We only discuss about the coursework and preliminary exam requirements above. For me, what also constitutes a big chunk of "hardness" is whether you'll be able to find a strong advisor that you like, whether you'll be allowed to start research asap etc, etc. 

[bernoulli_babe]

@MathStat @Stat Assistant Professor
Thanks for your replies! 
I get the sense that a good number of students really struggle at certain programs or that they are overworked with homework. Some places seem to have grueling qualifying exams like UW. I read somewhere that if you struggle with Casella Berger then you're not well-equipped to do a PhD at a top program. I personally thought that Casella Berger was manageable but not easy.

For example, can a biology or CS major complete a top biostats or stats program granted we've had limited exposure to Real Analysis or Measure Theory? 

 

[StatsG0d]

10 hours ago, bernoulli_babe said:

@MathStat @Stat Assistant Professor
Thanks for your replies! 
I get the sense that a good number of students really struggle at certain programs or that they are overworked with homework. Some places seem to have grueling qualifying exams like UW. I read somewhere that if you struggle with Casella Berger then you're not well-equipped to do a PhD at a top program. I personally thought that Casella Berger was manageable but not easy.

For example, can a biology or CS major complete a top biostats or stats program granted we've had limited exposure to Real Analysis or Measure Theory? 

 

Any successful PhD student has likely struggled at several points in their training. My first year (Casella-Berger year), I barely managed a 3.5 while my peers were getting Mostly A's and A-'s (for the record, it's pretty hard to score below a B in grad school). After the first year, I was getting better grades than many of my peers who crushed me in the first year. The point of the PhD training is to tax you mentally so that you can start to mature mathematically.

I personally do not think grades or how well you do on the qualifying exam will make you a good researcher. It may be different in some old school professors' eyes, but I think most people these days view the qual / courses as a means to an end. At some point in your career as a graduate student, things will start to click together. And it's very possible you'll never see/use measure theory stuff ever again after taking it.

One of my peers was a bio major in undergrad, and ended up receiving the highest score on the theory portion of our doctoral exam. They had little/no previous exposure to real analysis. They are an extremely hard worker, so there's that. But all this to say, I think it's extremely possible for a bio / CS major to be successful in a statistics / biostatistics PhD program, albeit maybe the latter more than the former.

[DanielWarlock]

On 9/23/2020 at 9:40 PM, bernoulli_babe said:

@MathStat @Stat Assistant Professor
Thanks for your replies! 
I get the sense that a good number of students really struggle at certain programs or that they are overworked with homework. Some places seem to have grueling qualifying exams like UW. I read somewhere that if you struggle with Casella Berger then you're not well-equipped to do a PhD at a top program. I personally thought that Casella Berger was manageable but not easy.

For example, can a biology or CS major complete a top biostats or stats program granted we've had limited exposure to Real Analysis or Measure Theory? 

 

 

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). 

 

 

Edited September 28, 2020 by DanielWarlock