MathStat

Yeah your grades would matter a lot. Definitely for US PhD applications, as they care a lot about your ability to handle grad level courses, just like they care when you apply as an undergrad (you're essentially starting over your application progress, that's how "transferring" in the US works). For Europe PhDs I don't know for sure, but I am somewhat familiar with the UK system (applied there for both undergrad and grad but chose the US both times) and the UK is very grade-centric, they seem to value grades more than research/letters.

I don't want to be excessively nosy, but can't help and wonder: did the above-mentioned person do her Phd at Stanford or Berkeley or somewhere else? And yes, of course this is an amazing profile: http://web.stanford.edu/~songmei/. He was also one of the top picks for new faculty asst. profs. at UChicago.

Going back to the teaching positions question, what would it take to obtain a tenure-track teaching position at a top and very desirable LAC (such as, say, Pomona College) in terms of research/publications, assuming that you go to a top 10 program and can build a strong teaching portfolio? Would the above publication guidelines detailed by @Stat Assistant Professor (i.e. at least 2 papers in Annals/JASA, 6ish papers total, etc) still hold true? Also, would you still need a superstar advisor, or would it be ok to work with a very young advisor (who could become well known in a few years)? I plan to do some stalking by looking at the teaching faculty profiles at such places...but I want to put it off a bit, I suspect I will get scared by finding some super strong CVs.

I agree, nobody cares but Stanford. Not even Chicago, Berkeley, UW, Columbia etc etc. Although as a funny story, one of the Duke stat faculty told me they like to see the score if you wanna do research in math stat. Duke also used to say on their website not to bother with sending the scores as they won't look at them. Gotta love academia

why is there almost no info on these folks, though? sounds like this would be the place for more mathematical/statistical research https://www.microsoft.com/en-us/research/group/theory-group/

Of course they should first of all think very well which program they really want to attend. It is possible to experience strong second doubts before starting your program, especially when you had several fantastic options or in circumstances where your decision was constrained by other factors which made it very difficult. furthermore, as horrible as this sounds, i think it is quite common that current students start disclosing more of the "ugly sides" of your future program after you have already committed and it is too late to do anything (as opposed to the visit day, where they only reveal positives). from my experience, these profs understand it is such a difficult and life-changing decision and I think they would truly want you to end up at a place that fits you. I also have the cynical belief they do not care that much about you (just yet) so it's not a big deal if you say you will attend a program, and then back out. I think it would be possible to first discreetly ask the other department whether they would take you back, then plan your next steps accordingly. edit: sometimes, departments decide to offer you more money right at the last minute, during the weekend before april 15th, after you already committed to a program. this puts you in a very difficult spot and you can imagine that, if you decide to be ethical and stick by your initial choice, you would still experience second doubts creeping in...

@incomingstatsPhD, have you tried contacting the program you are regretting turning down? if not, i think you should do it like RIGHT NOW, and just explain your reasons honestly. it's still may, perhaps there is still time to revert this. you'll never know if you don't try.

what is the other option (15%)?

While I am obviously biased since I did my undergrad there, I think Duke is very special since it is wayyy easier to collaborate with faculty there and get started on research early. Unfortunately, many programs hold you back with classes in the first 1-2 years (some classes arguably useful and important, others...not so much...). However, I see Duke stat phds get started on research straight away and even have preprints by the beginning of their second year! NCSU is a very strong university, however, I did not like the huge size the program and the fact that the DGS told me no prof really wants to do research with you before you pass exams (something that happens at even bigger name places as well...).

I see, this may indeed be a sad reality for international students and so the solution may indeed be doing whatever masters you can get that has a strong researcher there to discover you and believe in you.

I personally do not think average grades in multivariable calculus and linear algebra are so dramatic. For what it is worth I got B+ in multi because I couldn't do the tricky double/triple integrals fast enough during the midterms. This did not affect my phd admissions but granted I had grad level math courses with As to make up for it. Linear for some is also a tricky course the first time you see it. If I hadn't struggled with it before in high school I would've also gotten a B in my first semester linear class. I would say it is perfectly understandable to mess up the math classes in your first year if you didn't have exposure before. " My grades in stats, machine learning and CS classes have all been A/A-'s and one or two B+'s. " <- that is very good. The nice thing I notice about the United States is the culture of "second chances". The postdoc who helped me immensely to develop beautiful undergrad research also messed up his linear/multi and other math classes in undergrad. He said he had mostly bad grades during his math BS at UW. He stayed for a masters to fix his background, then did very well in his math phd and was able to secure a postdoc at duke with one of the best probabilists in the world. There are tons of similar examples from stat, including a stat professor I had at duke who climbed her way up from not prestigious undergrad and masters to phd at UFlorida, then postdoc at CMU, then tenure track at Duke. There is hope @L2norm! Many established and strong researchers were able to not let their "humble" beginnings define their future. The issue is that all that I know of are American citizens...so i can't really comment about internationals. The question is: since your gpa is rather low but your math/stat/cs course grades are pretty good since you only have 2 Bs, it means you messed up most of your humanities/social sciences classes. Do stat programs really care about this?

Hi, For the thesis, I've seen people do cutting edge research and publishing a paper out of their thesis, as well as others that just do a literature review, or some data analysis. So i'd say there's a pretty big variety in the types of theses. You are right, publishing a paper from the masters thesis is ideal for phd admissions (lit rev and data analysis are pretty useless IMO). However, for industry I can't say how useful a thesis is, maybe it is if your thesis work is original and related to your future job. "Also could you please tell me something about the career fair for stats students?" - I haven't participated in the fair this year as I'm not doing an internship this summer...all I can say is that unfortunately, the big tech companies such as Google, Amazon, Microsoft etc... will *not* be there to recruit. I heard google/maybe other sometimes come to the CS dept to recruit; for tech I would say some profs from TTIC and CS with which you can build relationships by taking classes with are your best bet to help you connect with such big tech firms; yes, our recruitment is not ideal, but I would say you should also talk to more knowledgeable people than me. "And finally about the summer internship, did most of the students choose to do the internship just in Chicago?" - hmmm, now that I think about it, I am not sure. it definitely is convenient to do it in Chicago since leases here last 12 months and it's a pain to have to either sublet over the summer while you are away for your internship or double pay both rent here and in your new summer location. However, I do know about phd students who did pretty good internships (including google) in other cities. Obviously, the masters students will now much more than what I said above, so it's good to also reach out to them. Also, definitely ask these questions + placement statistics to Mei Wang the masters coordinator - she is a very nice person. Hope this helps. Edit: one last thing that came to mind is that i know of undergrads here who got full time data science positions at Facebook...so if they could do it, then masters students should be able to, right?

As someone who chose Chicago versus UW (for phd not masters though), I'd also be curious to know whether there would be more tech opportunities for tech at UW (despite this info being useless for me at this point, haha...still, interesting to know). As far as I know from my masters peers here, there are two options for classes at UChicago: 1. the hard path - take the phd -level sequences of applied stat and math stat (and probability, if you want). Stat 304 = distribution theory which is a core phd class taken during the first quarter is absolutely brutal and I heard many of my phd peers got less than ideal grades in it (I luckily was able to place out of it, by taking a Brownian motion class with the famous Greg Lawler, which was absolutely beautiful). Still, I know several people who chose this option, worked hard, got a very strong background (I personally think most phd classes, except STAT 304, are very reasonable), and were admitted to very good PhD programs. 2. the less brutal path - take more typical masters level classes, which could include CS and ML classes from the CS dept or from the Toyota Technological Institute (which offers fantastic classes IMO). People who did this perhaps had a less stressful life, got very good grades, and still managed to get into great PhD programs. So it seems to me that this option is not necessarily worse for the purpose of stat grad school admissions. If you count the fact that you have a little bit more flexibility to take more CS/ML classes and prepare more for industry, this seems like the better option to me. I think most if not all masters students here do pretty good summer internships in the summer between their two years. Also, while i think it would be hard to graduate in a single year (due to also having a thesis requirement in addition to the requirement of 9 courses), I think it is very doable and realistic to graduate in 1.5 years. There is a student I know of who did that and got a Data Science position at Microsoft (Seattle!!!). Also regarding tech jobs, I heard people can generally get them, but they turn them down for much better paid finance jobs here in Chicago. Again, can't comment if people here get *as many* good tech jobs as people at UW. Regarding applied/practical work, in addition to some classes that involve pretty useful class projects during the year (such as a course on "Multiple Testing", or some CS classes), we also have mandatory "consulting" projects, both for masters and phds. These involve working in a group of Phd and masters students to come up with a statistical solution for a client's applied problem (the clients here are generally PhD students from other depts needing further statistical support and analyses for their dissertation research; they usually come with already collected data). SO I think you can get practical experience here, despite the core courses being more theoretical. But perhaps you can get even more practical work done at UW, haha, and I'd again be curious about that. Hope this helps, let me know if you have other questions.

First of all, congratulations @Stat PhD Now Postdoc for finding an academic job! That is amazing news and I am very glad to hear that. Thank you everyone for all your insights. I think my approach right now would be to enjoy the opportunity given to me to focus for 5 years on work and research I find interesting and see what happens later. One of my classmates who is a masters student and does not plan to pursue a PhD also tells me that you *can* find interesting work in industry without a phd and perhaps without even a masters, as was his experience - the issue is that the first one or two years may be more boring work, until you convince your colleagues that you have original ideas that are worth pursuing. However, if you have valuable ideas that you can show lead to promising results which could benefit the business, you will be allowed to branch out and conduct your own work (more of less), and thus gain more independence. This is in the finance industry. I don't yet know that much about tech, but hopefully there can be similar stories in tech too.

Thank you, @bayessays, for your insightful and honest comment, as always. While I barely started my Stat PhD, it is sad and discouraging to read this (despite hearing this before and sort of being aware of the issue) : "PhDs get the same boring data science job as people with master's degrees." Of course most of us working towards the Phd love stimulating and intellectually challenging work and we will try our best for a good / decent academic placement. However, as it has been discussed here before, with fantastic information and tips from @Stat PhD Now Postdoc, academic jobs are simply put, a big lottery for the most part. Even if we assume you played your cards right and secured a great academic placement, you have to deal with the "tenure chase" stress for several years. One world-class famous professor from my undergrad told me that he doesn't know whether he would choose this same path again. At the other extreme, many people around me (including the original poster, @Bacaw) have described their data science roles (ranging from small to fancy, big-name companies) as repetitive, menial, boring work, where they use the same methods/tools almost every day. I admit I am not very knowledgeable about industry jobs and perhaps you can still find a fulfilling and stimulating data science position if you look hard enough. These descriptions are also very subjective and depend a lot on the individuals' preferences and visions. The question is: is there any middle ground option that is perhaps not as extreme as the two above-mentioned paths? Would top-notch research positions in industry such as Microsoft research, Facebook AI research, Google research etc. be the answer? I heard the hiring process for Microsoft research is nearly as competitive and rigorous as that for a top academic job. If anyone has any experience on this and is willing to share, I would love to hear tips about preparing for such industry research roles. And of course, if people think there are other answers to the question of which kinds of jobs (if any) would have both the perks of industry and academia, I would really appreciate the advice. Thanks everyone in advance and sorry for hijacking this thread with a somewhat personal worry. However I think it is reasonably relevant to the topic discussed here.