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Hey everyone! I wanted to get a better sense of what my chances are for an MS in statistics at some top programs. I intend to use my MS experience to improve my profile for a Statistics or CS PhD and to also figure out if I want to go down the PhD route or not. My area of focus in undergrad research has been statistical genetics and biostatistics/computational biology type stuff and I'd like to continue this in grad school.

One caveat is that I'm currently a junior so I won't have my senior year grades when I apply in the fall. So my profile below is based on everything I've done so far. 

Undergrad Institution: Berkeley

Undergrad Major: Bachelor of Arts in Mathematics and Statistics 

GPA (Undergrad): 3.84 Overall GPA, 3.85 major GPA

Type of Student: Domestic

Relevant Courses(Undergraduate): 

Freshman Year: Calculus II (B), Introduction to Programming (B+), Multivariable Calculus (A), Linear Algebra and Differential Equations (A-), Foundations of Data Science (A-)

Sophomore Year: Probability Theory (A), Real Analysis (A), Complex Analysis (A), Statistics (A), Abstract Algebra (A),

Junior Year: Analysis II (A), Abstract Linear Algebra (Pass), Mathematical Optimization (Pass), Introduction to Functional Analysis (A), Partial Differential Equations (Taking this summer)

Senior Year: Statistics Honors Thesis (A), Math Honors Thesis (A)

Relevant Courses(Graduate):

Junior Year: Statistical Genetics (A), Mathematical Population Genetics (A)

Senior Year: Measure-Theoretic Probability Sequence (Stat 205A+Stat 205B), Applied Statistics PhD Sequence (Stat 215A+Stat 215B), Algebraic Topology, Differential Manifolds 

Relevant Research: Research under one EECS and one EECS/Stats professor.  I've been conducting research into developing visualization and statistical methods for high-throughput genetic data and also in mathematical population genetics (things like allele dispersion). I might have a publication by the time I apply, working hard on that paper this summer. I've also written honors thesis for the statistics major and math major. The stats thesis focuses on statistical methods for genetics and the math one is on stochastic analysis in population genetics. 

Recs: 2 very strong letters of rec from my research advisors. A third regular one from my Real Analysis professor. 

GRE General: V/Q/A: 167/168/5 

Relevant Work Experience: I spent my freshman summer interning as a software engineer at a startup. I spent my sophomore summer as a software engineering intern at Google. 

Programs Applying: Stanford MS in Statistics, UChicago MS in Statistics, Oxford MSc in Statistical Sciences, Cambridge MASt in Mathematical Statistics 

Current status: Wrapping up my junior year and doing research this summer 

 

I have 3 main concerns I was hoping to hear perspectives on- 

1. I took Abstract Linear Algebra and Optimization Theory for a Pass grade (not letter grade) this semester because of COVID-19. The math department sent out a mail advising everyone to take all classes Pass/No Pass so that's exactly what I did. I've heard Abstract Linear Algebra is incredibly important for Stats grad school, and I only got an A- in lower div Linear Algebra+Diff Eq's. How much will this hurt me? 

2. I have a lack of programming classes, and in the only programming class I took at Berkeley, I received a B+ (my studying habits were nonexistent freshman year). However, I've been programming for several years now and I have two software engineering internships (one at Google) to back this up. Will I be ok in this regard?
3. I've heard publications are really important for grad school. I have strong research experience, but I'm not sure if my paper will be published by the time I apply. Will this be held against me for these top programs?

 

So that's my entire profile. I was hoping to gauge my chances for the programs I'm applying to and if there's anything I can do to improve my chances before I apply this fall. Like I said, I want to use this MS to improve my CS/Statistics PhD profile and to also figure out if grad school is for me. I'd also to love to hear if there are any other programs I should apply to. Thanks so much!

Edited by mahof
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I would just apply for a PhD if I were you, you've got plenty of research experience, especially if you do indeed take+do well in the Berkeley PhD core courses 205A/B, 215A/B. Sounds like you'd have a shot at the (stats) PhD programs for those schools (Stanford may be a stretch), so getting in for a MS shouldn't be a problem.

I'd really just talk to the PhD students/professors you've been working with - they'll know how strong of a letter they will write for you, which noone else does.

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Absolutely agreed with above. If you think you might even possibly want a PhD.  You'll get into tons of top 20, and very very likely top 5/10 programs as is.  I don't see a MS helping your profile enough to be worth the time or money. 

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18 minutes ago, insert_name_here said:

I would just apply for a PhD if I were you, you've got plenty of research experience, especially if you do indeed take+do well in the Berkeley PhD core courses 205A/B, 215A/B. Sounds like you'd have a shot at the (stats) PhD programs for those schools (Stanford may be a stretch), so getting in for a MS shouldn't be a problem.

I'd really just talk to the PhD students/professors you've been working with - they'll know how strong of a letter they will write for you, which noone else does.

Of course the OP has his eye on Stanford (especially with such a magnificent profile). And yes, a sure way to secure a spot there as a PhD is to attend their master program in statistics. Your chance is superb. Good luck.

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@insert_name_here, @bayessays, @DanielWarlock. Thanks for the feedback everyone! So you don't think that taking Abstract Linear Algebra + Mathematical Optimization Pass/No Pass this semester because of COVID-19 will hurt me? Or my lack of dedicated programming classes in college? Those are two things that have been worrying me a lot.

Phew, it's reassuring to hear that you guys think I'd be competitive for a top PhD program as my profile stands right now. I guess there are two reasons I'm a bit hesitant to apply for a PhD right now. The first is that even though I enjoy my research a lot, I'm not sure if I'm cut out for the rigors of a PhD and if I'd even want to be in school for 5-6 more years. Thus, my thinking is that a masters will help me figure out if I want to continue on with grad school. The second reason is that I might choose to apply to some top CS PhD programs like Berkeley, Stanford, MIT, Princeton, etc. because these departments do a lot of research work in my areas of interest too. I've read online that the competition to get into these programs is super intense and having publications before applying can be very helpful for CS PhDs. So my thinking is that if I do a MS before my PhD applications, I have more time to publish and strength my app that way. What do you think? Thanks so much in advance!

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You could've stopped taking math classes after your sophomore year and still had enough math for top 10 programs.  You have enough As now after your junior year, even with the pass/no pass, that programs aren't going to doubt your math ability. Dedicated CS classes aren't a necessity, but being able to do some programming helps - a Google internship will be more than sufficient. 

I'm not super familiar with CS admissions but have heard some of the same things. I think you can get into a lot of really good stats PhDs that have professors affiliated with CS departments/ability to work across departments. The biggest advantages this way are that you can save a year or two of your time, as well as save $100k by not having to pay tuition for your master's. So the opportunity cost of doing master's could be around $250k, and you can always drop out after two years with MS if you need to, or transfer programs in a worst case scenario. 

 

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I'd also add that many masters programs aren't really a "PhD-preview", but more of an extra year/two of undergrad, in that they are filled with coursework.

You have plenty of math, don't worry about it. Same for programming.

Having a paper is nice, but plenty of people get into top stat programs without them. If your professors says nice things about you that's what really matters.

4 hours ago, DanielWarlock said:

 

Of course the OP has his eye on Stanford (especially with such a magnificent profile). And yes, a sure way to secure a spot there as a PhD is to attend their master program in statistics. Your chance is superb. Good luck.

I don't want to start a fight here, but I don't think this is true, or the best advice. E.g. at Berkeley, I don't think they've accepted any Berkeley masters students into the stat PhD program (1 or 2 may have gotten into biostat), out of the 40+ masters students per year. I may be wrong, but I would be very surprised if, in the past five years, Stanford has accepted more than 1 student out of their masters (out of maybe 100-200 masters students in those 5 years), and my guess would be they haven't taken a single one. Regardless, I would encourage anyone (OP or future people reading) to thoroughly vet claims like this before you use them as part of a big life decision.

While there are some exceptions, the standard path in statistics is, by and large, to go straight to a PhD from college. Especially for domestic students.

For CS it is a bit less uncommon to take time off, but (for applied ML folks at least), the more common approach is a AI residency at big tech cos (Google, FB, etc) rather than taking a bunch of courses in a masters.

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4 minutes ago, insert_name_here said:

I don't want to start a fight here, but I don't think this is true, or the best advice

You are correct that this is bad advice. Stanford explicitly tells applicants in their FAQ that you should not apply to the MS program as a path to their PhD program.

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Agree with most of what's been posted above before: you should probably be thinking about applying to PhD programs rather than masters. I'm currently a senior at Cal and you have a pretty similar profile with me and I got into several top PhD programs in statistics. However I'd be a bit careful this coming application season since of the Covid-19 induced recession, it seems like schools are planning for smaller cohort sizes (a rumor that I've been hearing going around but unconfirmed) so it might be tougher to get into top programs compared to last applicant season.

I'd swap out 205 for 210 though; primarily because the content you see in 205 you will be able to see anywhere else, but the content covered in 210B is very unique to Berkeley and I think you really would be missing out if you didn't take advantage of the opportunity to take it while you are still at Cal.

PM me if you have any further questions.

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@icantdoalgebra thanks for the advice! What's 210B and why would it be useful for me? I took a quick glance at the prereqs and apparently it needs 205B too. The reason I was thinking of doing 205AB instead is because it is more directly aligned with my interests in population genetics.

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210B just requires some basic martingale stuff from 205A (sometimes not even). I took 210B without 205A/B and I was fine.

The reason I recommended 210B is that you can get 205 anywhere: every measure theoretic probability theory course will cover basically the same material. However 210B is really a Berkeley specific course and you would have a hard time finding a similar course elsewhere; for example Berkeley's Stats is really big on non-asymptotic results and 210B introduces a lot of the tools and techniques for non-asymptotic results so you would get a decent idea of some of the research approach that people in Berkeley specialize in. I think that if you have an opportunity to take this instead of 205, which you can just take the equivalent at any university you go to, you should.

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