bob loblaw

Some thoughts Not failing your required classes is important... but no one cares about your Graduate GPA If it's the first time they're offering this class, reasonable professors will probably dismiss a low grade as a noisy measure of your capabilities Suppose I'm wrong. Do you want to be advised by someone that is that fixated on you performing poorly in this class that your entire cohort thought was unreasonably hard? Spending time on pursuing your research interests seems to be a pretty important priority as a PhD student That said, I'm a second year PhD student as well so you might want to ask other people in your program/professors.

@manofpeace Absolutely. Having a PhD is obviously a prerequisite now for any research oriented industry role. But also given the increased competition for data science roles, I think a PhD will be valuable there as well. WRT to math, it's not all or nothing. My view is that you should at least have taken proof-based linear algebra and real analysis at a minimum. Having other classes like stochastic processes, convex optimization, measure theory, etc. will make you a more competitive candidate. Of course, the math classes you choose to take should make sense given your research interests. For example, taking an abstract algebra course may kind of seem random for some but may make sense if your interest lie in random matrices. Different programs (bio stats included) care about mathematical background to varying degrees: more theoretical a given department is (which tends to be higher ranked or whatever), the more they'll care. It also depends on how competitive your application year happens to be: UCLA Biostats, for example, weeds people out by mathematical background especially in competitive years. My program is more on the applied side so it doesn't care that much.

With all due respect, your advisor seems like the archetype of an out-of-touch academic (probably a boomer). Sorry to hear this. If I were you, I would not let him hold you hostage, ignore sunk-costs and start establishing relationships with professors you've taken courses from. It's not the end of the world to have mediocre LORs. I personally did not have great LORs but it was fine for me. Like the previous comment said, however, having a firm mathematical foundation is important (especially in a program that emphasizes probability). Also if you're uncertain about a PhD program BEFORE you apply, those thoughts are gonna be amplified once you're in the program. I'd keep that in mind.

I was in the same situation you were in. Taking courses as a non matriculated student is generally painful. I'd take a course from UIUC's NetMath! It was just what I needed and the transcript you get is identical to UIUC's (basically).

The workload varies a lot but 90 is excessive. My upper bound so far has been 60 hrs. There's also a general culture in PhD programs of over-working and doing way more than necessary IMO.

You have a great background! I would cut down on your list of schools to 7 or 8. Maybe cut some reach schools? Other notes: UCLA Biostats may be a better fit since you're more interested in applications. If you're interested in Bayesian stuff, I'd recommend UCSC as a match school. You'd most likely get in. This is completely based on "what I've heard" but UC Irvine is actually more difficult to get into than rankings suggest.

You will definitely get in somewhere for sure! What is your eventual goal? PhD? Getting a job after MS?

Your GPA in quantitative courses will matter most. Even among courses, I assume it is weighted on relevance: a B- in numerical optimization is different than a B- in probability

Hey all, I summarized my take on applying for grad school. My guidance may be more useful for “atypical” candidates or candidates whose undergrad math background is not particularly deep. I wrote most of this last year but I made recent edits to add guidance for Master's students. https://sho-kawano.github.io/2022/01/07/grad_school_guide/ Hope this is helpful to someone out there!

It highly depends on the program! ? For example, at my institution, a Master's Student isn't really expected to have an extensive background. They even cover basic probability in their first quarter. Lemme know if you have other questions!

Your MS choices look fine to me. I think your PhD program choices are total reaches. If you applied to lower ranked PhD programs you'd probably get in to one. I think that would be better personally because PhD programs provide funding

I think UC's are more competitive than the rankings suggest. If you are interested in Biostats because you're more drawn to applied research, UCSC and UCSB are also great choices. I also believe UC Davis allows you to apply to both Stats & Biostats. Since the departments share professors & the courses, I'd suggest applying to both if you really want to go to Davis. Given your mathematical background, I really think you will have somewhere to go to. My mathbackground is much inferior and I got into 3 places last year.

Thanks for commenting @BL4CKxP3NGU1N . That's reassuring since it costs so much to get one. Also @bayessays Idunno if you got the latest one but that macbook air doesn't even have a fan? So apparently they have to throttle the CPU for thermal issues.

Considering getting a M1 Macbook Pro. Besides the cost I'm concerned about potential problems with not being able to use some R packages due to compatibility issues with Apple Silicon. Anyone have any thoughts on this? Besides tidyverse I'd imagine there isn't a whole lot of packages I would need for classes... For full context, I go to a very Bayesain program so I'm guessing I'll use my CPU a fair amount.?

With your background, you may even want to apply to PhD programs. These programs will fund your studies. You can then decide later if you want to just take the masters and leave or complete your PhD (which can get you research positions in tech).