icantdoalgebra

Easy; Peter Bickel, Michael Jordan, Martin Wainwright. People like van der Laan, Bartlett, Brillinger, Aldous, Yu, and Pitman might not be as famous as those Stanford faculty that you listed, but its not like they are some random professors. How about Fernando Perez and his work on creating Jupyter Notebooks? Perhaps its not as much of a research accomplishment, but creating a widely-used software that makes performing statistics and data science easier is definitely worth quite a bit of influence.
I'm not trying to argue that Berkeley is better, in fact I agree that Stanford's program is better. However is it substantially better? I am skeptical. 
There is a valid point though, in that Berkeley's stats department has leaned more heavily towards machine learning recently and if you are a statistics purist you could reasonably make the argument that Stanford is substantially better if that is your criteria for evaluation.

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!

Most beginning PhD students do not have this, so don't worry.
 
Read the first couple paragraphs on Wikipedia.
 
There is no reason for you to need to understand open problems in areas you will not be studying.  

Isn't that more due to the fact that MIT primarily only has people in either ML or high-dimensional statistics (and in this case I feel like the opportunity of working with Candes at Stanford or Wainwright at Berkeley is probably better than being at MIT), but there's more to statistics than these two fields: what if the person wanted to do something in causal inference or post-selective inference? MIT would clearly be not as great compared to other top stats programs. Additionally what makes a good EECS application is pretty different from what makes a good stats application: applying to EECS in ML usually revolves around having published somewhere in ICML or NeurIPS (sometimes even multiple times) as an undergrad, and comparatively grades/coursework really don't matter that much. 
 
Perhaps this is true in general about stats program curricula being outdated but if we are comparing Stanford/Berkeley to MIT this isn't really that true nowadays. People in the stat department who are interested in ML are encouraged to go take courses in optimization and information theory (which is what I am doing) because as you said, they are very useful. In fact one of the core courses at Berkeley gives a brief introduction to information theory because of how useful it is. But again, not everybody wants to do ML/data science/what-ever gets hyped up in the media nowadays and places like MIT EECS/Math or Princeton ORFE are really niche recommendations since they tend to lean very heavily towards a small subset of areas within statistics and have virtually no presence elsewhere. Also about your point for professorship, I know Berkeley gave out 3 offers for faculty last year, and 2 of them are for somebody with a traditional statistics background.
@kimmy sorry for the sidetracking but the people on this forum are pretty good at giving advice on applying to stats programs (probably because there are a few stats professors running around on these forums). I'd be somewhat more skeptical of any advice you'd receive here about applying to math or EECS (as to the best of my knowledge) because there aren't any similar such people on this forum and you should probably go look elsewhere for advice but do consider these options if that is something you are interested about. 

Last year I've asked for an extension on April 15th due to getting an offer very late in the cycle.
IMO it doesn't hurt to try and ask at least; the most likely worst-case scenario is that your request for extension gets denied (I would be quite shocked if anything worse than this happens) but there is a chance that they would grant you an extension.

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

As a side note to this point, a lot of Berkeley's most famous statisticians have passed away. Note this is not particularly relevant to the current discussion at hand since we are discussing the departments today (and also because ... dead people don't do research), but if we do want to talk about "revolutionary" developments I think it is somewhat worthwhile to bring up Berkeley's significance (although it really shouldn't affect anybody's decision in where to go or departmental rankings because this is quite historical).
Jerzy Neyman was a professor at Berkeley and was the key driving force behind the formation of the department and he was no slouch when it came to revolutionary developments (quotes deliberately omitted because confidence intervals, hypothesis testing, potential outcomes are all, beyond any doubt, revolutionary developments).
George Dantzig was a student and professor at Berkeley for a while (although he left for Stanford later) but while at Berkeley he developed the simplex algorithm which may not be revolutionary in statistics, but is the foundation for several other fields.
David Blackwell was a professor at Berkeley for several decades; you might know him from Rao-Blackwell, but he also did a lot more than that (Blackwell-Macqueen, etc.).
There are a few others that can be mentioned too, David Freedman has had quite a bit of influence outside the world of statistics (and in statistical education), Leo Breiman has been mentioned above (CART and random forests), Le Cam and his work on lower bounds, and several more. 
I bring them up not because I'm trying to argue against any of the positions taken here: I agree that Stanford's statistics department is better today. It is in a class of its own when you consider traditional statistics. But I still feel somewhat obligated to defend Berkeley if we are mentioning "revolutionary" developments without mentioning Berkeley's own rich history. However let me re-emphasize, none of the above should be a part of one's consideration for picking a program for grad school nor should it be a part of decision making about which departments are stronger.

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.

Easy; Peter Bickel, Michael Jordan, Martin Wainwright. People like van der Laan, Bartlett, Brillinger, Aldous, Yu, and Pitman might not be as famous as those Stanford faculty that you listed, but its not like they are some random professors. How about Fernando Perez and his work on creating Jupyter Notebooks? Perhaps its not as much of a research accomplishment, but creating a widely-used software that makes performing statistics and data science easier is definitely worth quite a bit of influence.
I'm not trying to argue that Berkeley is better, in fact I agree that Stanford's program is better. However is it substantially better? I am skeptical. 
There is a valid point though, in that Berkeley's stats department has leaned more heavily towards machine learning recently and if you are a statistics purist you could reasonably make the argument that Stanford is substantially better if that is your criteria for evaluation.

Easy; Peter Bickel, Michael Jordan, Martin Wainwright. People like van der Laan, Bartlett, Brillinger, Aldous, Yu, and Pitman might not be as famous as those Stanford faculty that you listed, but its not like they are some random professors. How about Fernando Perez and his work on creating Jupyter Notebooks? Perhaps its not as much of a research accomplishment, but creating a widely-used software that makes performing statistics and data science easier is definitely worth quite a bit of influence.
I'm not trying to argue that Berkeley is better, in fact I agree that Stanford's program is better. However is it substantially better? I am skeptical. 
There is a valid point though, in that Berkeley's stats department has leaned more heavily towards machine learning recently and if you are a statistics purist you could reasonably make the argument that Stanford is substantially better if that is your criteria for evaluation.

If any stats program decided to do this, you would instantly know to avoid it because this is, by definition, extremely horrible statistics.
The goal of an admissions committee is to try and pick the candidates that has the highest future success (of which is measured to whatever the committee decides to chose) subject to a series of resource constraints. Ultimately there is a regression problem involved: trying to predict future outcomes given the applicant profile. You are proposing for the committee to throw out all covariates that may have predictive power on the outcome and replace it with a single measured value (the proposed test score), whereas any statistician with half a brain would propose the exact opposite.
The admissions committee may not be running a linear regression or some ML algorithm to try and predict what will happen to candidates in the future, but the point still stands. Having more than just one feature to look at is often superior to only looking at one feature and trying to predict with that.

If any stats program decided to do this, you would instantly know to avoid it because this is, by definition, extremely horrible statistics.
The goal of an admissions committee is to try and pick the candidates that has the highest future success (of which is measured to whatever the committee decides to chose) subject to a series of resource constraints. Ultimately there is a regression problem involved: trying to predict future outcomes given the applicant profile. You are proposing for the committee to throw out all covariates that may have predictive power on the outcome and replace it with a single measured value (the proposed test score), whereas any statistician with half a brain would propose the exact opposite.
The admissions committee may not be running a linear regression or some ML algorithm to try and predict what will happen to candidates in the future, but the point still stands. Having more than just one feature to look at is often superior to only looking at one feature and trying to predict with that.

If any stats program decided to do this, you would instantly know to avoid it because this is, by definition, extremely horrible statistics.
The goal of an admissions committee is to try and pick the candidates that has the highest future success (of which is measured to whatever the committee decides to chose) subject to a series of resource constraints. Ultimately there is a regression problem involved: trying to predict future outcomes given the applicant profile. You are proposing for the committee to throw out all covariates that may have predictive power on the outcome and replace it with a single measured value (the proposed test score), whereas any statistician with half a brain would propose the exact opposite.
The admissions committee may not be running a linear regression or some ML algorithm to try and predict what will happen to candidates in the future, but the point still stands. Having more than just one feature to look at is often superior to only looking at one feature and trying to predict with that.

how is harvard a good fit given your research interests? I feel like they're more into biostat/applied stuff..although Cynthia Dwork is there...
If you're into probability, deep learning etc, then berkeley and potentially other schools out of those 24 would be better fits. 
 
 

Berkeley last year did a batch in February and a waitlist batch in April.
Rejections usually come a few weeks later in the cycle.
Correction: Berkeley apparently doesn't officially do waitlists; usually to admit more people they have to get approval.

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.

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.

I didn't take multivariable calculus when applying last year and didn't have trouble getting into programs.
I think having more advanced classes should make up for not having an intro class like multivariate, but that's just my guess and maybe you should take it to be on the safe side? I'm not too certain what the correct move is.

Isn't that more due to the fact that MIT primarily only has people in either ML or high-dimensional statistics (and in this case I feel like the opportunity of working with Candes at Stanford or Wainwright at Berkeley is probably better than being at MIT), but there's more to statistics than these two fields: what if the person wanted to do something in causal inference or post-selective inference? MIT would clearly be not as great compared to other top stats programs. Additionally what makes a good EECS application is pretty different from what makes a good stats application: applying to EECS in ML usually revolves around having published somewhere in ICML or NeurIPS (sometimes even multiple times) as an undergrad, and comparatively grades/coursework really don't matter that much. 
 
Perhaps this is true in general about stats program curricula being outdated but if we are comparing Stanford/Berkeley to MIT this isn't really that true nowadays. People in the stat department who are interested in ML are encouraged to go take courses in optimization and information theory (which is what I am doing) because as you said, they are very useful. In fact one of the core courses at Berkeley gives a brief introduction to information theory because of how useful it is. But again, not everybody wants to do ML/data science/what-ever gets hyped up in the media nowadays and places like MIT EECS/Math or Princeton ORFE are really niche recommendations since they tend to lean very heavily towards a small subset of areas within statistics and have virtually no presence elsewhere. Also about your point for professorship, I know Berkeley gave out 3 offers for faculty last year, and 2 of them are for somebody with a traditional statistics background.
@kimmy sorry for the sidetracking but the people on this forum are pretty good at giving advice on applying to stats programs (probably because there are a few stats professors running around on these forums). I'd be somewhat more skeptical of any advice you'd receive here about applying to math or EECS (as to the best of my knowledge) because there aren't any similar such people on this forum and you should probably go look elsewhere for advice but do consider these options if that is something you are interested about. 

Isn't that more due to the fact that MIT primarily only has people in either ML or high-dimensional statistics (and in this case I feel like the opportunity of working with Candes at Stanford or Wainwright at Berkeley is probably better than being at MIT), but there's more to statistics than these two fields: what if the person wanted to do something in causal inference or post-selective inference? MIT would clearly be not as great compared to other top stats programs. Additionally what makes a good EECS application is pretty different from what makes a good stats application: applying to EECS in ML usually revolves around having published somewhere in ICML or NeurIPS (sometimes even multiple times) as an undergrad, and comparatively grades/coursework really don't matter that much. 
 
Perhaps this is true in general about stats program curricula being outdated but if we are comparing Stanford/Berkeley to MIT this isn't really that true nowadays. People in the stat department who are interested in ML are encouraged to go take courses in optimization and information theory (which is what I am doing) because as you said, they are very useful. In fact one of the core courses at Berkeley gives a brief introduction to information theory because of how useful it is. But again, not everybody wants to do ML/data science/what-ever gets hyped up in the media nowadays and places like MIT EECS/Math or Princeton ORFE are really niche recommendations since they tend to lean very heavily towards a small subset of areas within statistics and have virtually no presence elsewhere. Also about your point for professorship, I know Berkeley gave out 3 offers for faculty last year, and 2 of them are for somebody with a traditional statistics background.
@kimmy sorry for the sidetracking but the people on this forum are pretty good at giving advice on applying to stats programs (probably because there are a few stats professors running around on these forums). I'd be somewhat more skeptical of any advice you'd receive here about applying to math or EECS (as to the best of my knowledge) because there aren't any similar such people on this forum and you should probably go look elsewhere for advice but do consider these options if that is something you are interested about. 

Isn't that more due to the fact that MIT primarily only has people in either ML or high-dimensional statistics (and in this case I feel like the opportunity of working with Candes at Stanford or Wainwright at Berkeley is probably better than being at MIT), but there's more to statistics than these two fields: what if the person wanted to do something in causal inference or post-selective inference? MIT would clearly be not as great compared to other top stats programs. Additionally what makes a good EECS application is pretty different from what makes a good stats application: applying to EECS in ML usually revolves around having published somewhere in ICML or NeurIPS (sometimes even multiple times) as an undergrad, and comparatively grades/coursework really don't matter that much. 
 
Perhaps this is true in general about stats program curricula being outdated but if we are comparing Stanford/Berkeley to MIT this isn't really that true nowadays. People in the stat department who are interested in ML are encouraged to go take courses in optimization and information theory (which is what I am doing) because as you said, they are very useful. In fact one of the core courses at Berkeley gives a brief introduction to information theory because of how useful it is. But again, not everybody wants to do ML/data science/what-ever gets hyped up in the media nowadays and places like MIT EECS/Math or Princeton ORFE are really niche recommendations since they tend to lean very heavily towards a small subset of areas within statistics and have virtually no presence elsewhere. Also about your point for professorship, I know Berkeley gave out 3 offers for faculty last year, and 2 of them are for somebody with a traditional statistics background.
@kimmy sorry for the sidetracking but the people on this forum are pretty good at giving advice on applying to stats programs (probably because there are a few stats professors running around on these forums). I'd be somewhat more skeptical of any advice you'd receive here about applying to math or EECS (as to the best of my knowledge) because there aren't any similar such people on this forum and you should probably go look elsewhere for advice but do consider these options if that is something you are interested about. 

I think you should clarify if its a school known for grade deflation or not. If its UChicago or Caltech the response might be different than if it were say Harvard or Yale. I would be overstepping my expertise if I tried to tell you what schools are a target vs reach but I could imagine the answers changing. Maybe others can comment on this perspective. 

You still have roughly 5-6 months before you need to apply; you can find a research opportunity in statistics at least by the time the fall semester starts, and doing a semester of stats research would get you a pretty strong rec letter. It shouldn't be that difficult to do considering your grades and the fact that you are at an Ivy league school.
Usually domestic students don't have meaningful theoretical research (although this is becoming less and less true) before applying to a PhD program, but international students at the top programs often do (to be fair I've only interacted with those at Berkeley but I'd assume it be similar at peer institutions). 
If you think that taking a gap year means that you get to apply later and have an easier time getting in due to Covid being over, I'd say just apply this year; there's really no guarantee things will be better. If you can do something in the gap year that would greatly improve your application, like a serious theoretical statistics research opportunity, its not a bad idea. 

I highly recommend the above. A $28.5k video card will be necessary for rendering ggplot outputs.

As an aside I think there are benefits to applying later in the cycle (especially as late as possible) if they don't mention that they do any sort of rolling admissions, as you can take the fall semester to improve your relationship with the professors writing your rec letters. I say this because I managed to get a research opportunity late August and the recommendation letter I got from that definitely strengthened my application profile considerably, but it doesn't have to be research. You could try and initiate a DRP with a professor writing you a rec letter or be a TA for one of their courses.