Jump to content

bayessays

Moderators
  • Content Count

    1,075
  • Joined

  • Last visited

  • Days Won

    31

bayessays last won the day on October 17 2020

bayessays had the most liked content!

5 Followers

About bayessays

  • Rank
    Cup o' Joe

Profile Information

  • Application Season
    2020 Fall

Recent Profile Visitors

11,517 profile views
  1. Yes, you will be well-prepared with those classes! The major isn't very important, the classes are what matters. Only a few top programs look at the subject test, so I don't think it will help you very much. Sending out 1-3 MS applications as back-ups in case PhD applications don't work out seems like a reasonable idea, and lots of people do that.
  2. I don't think biostatistics vs. statistics matters at all - you'll pretty much take the same courses at all these places. If you haven't taken real analysis, I would definitely go to a place where you can take that too to prepare for a PhD program. Do any of the programs give you a chance, in your first year, to start doing research with faculty so you can get a good letter of recommendation from someone? This to me would be by far the #1 factor in my decision. I do think that obviously there will be some small advantage in going to the higher ranked UCLA compared to UCSB, and also
  3. I think Colorado State is the only strong program that has a large core of spatial statistics people (about half their faculty).
  4. @Blain Waan (Besides the pharma positions mentioned above, which definitely need a PhD, I'm mostly talking about tech here...) I definitely agree that most jobs do not need a PhD. However, I would say there are quite a few jobs where a PhD is significantly more likely to have the skills necessary, even if a well-trained MS statistician could do the job too. At the end of the day, the student's PhD dissertation is likely not going to be the subject of their job anyways, so I don't really think a PhD is a true qualification for basically any job outside of bureaucratic requirements
  5. Agreed that it doesn't matter. You'll get good jobs regardless. I will say that some of the companies that are pickier about applicants though, and even a top PhD will not be a guarantee of a job. I have seen people from top 20 programs interview at FAANG who didn't know the basics of logistic regression, and at some of the top companies you really have to ace the interviews. But if you have a PhD from any stats program, and you are knowledgeable, I don't think any position is out of the question. I'm not sure how many people really get "research" positions though. I think Facebook has a
  6. I don't mean you need to work with a famous person (I suspect this doesn't hurt). But what I mean is there's a big difference between a small project where you do some data analysis for a class project vs. some applicants I see that have papers published in JASA before their PhD, and there is a broad spectrum in between. This will be reflected in the letters of recommendation.
  7. I think the higher ones are possibilities depending on what these papers and letters are like - if they worked with top people and did significant work, that's a totally different application. Hard to tell with details given, but that's a broad range of schools so you'd be fine.
  8. I'd email the grad coordinator and tell them they're your first choice but your other deadline is tomorrow and asking if they have an update.
  9. Good luck to everyone making last-minute decision before the April 15th deadline! I know it is stressful to make such a life-altering decision and it is easy to be overwhelmed. Just remember that it is impossible to predict the future (even when you have a PhD in statistics!) and all you can do is inform yourself as best you can and follow your gut!
  10. Math departments will want you to have classes like abstract algebra, so if you don't have that, it will be hard to get in. It's hard to find good probability groups in statistics departments outside of the top few programs (Stanford, Berkeley, Chicago, etc.). UNC has a lot of applied probability people, and Michigan State has a large group of probability people too. Outside of that, most programs will not have huge probability groups.
  11. "Thanks so much for your help throughout this process. It was a difficult decision, but I've decided to attend University X instead." They'll understand!
  12. I'm not sure of the state of the qualifying exams, but Michigan requires a year of PhD-level theory (the class after Casella-Berger you take first year), so I would think the coursework is not *that* different at the two places. I think they both will have enough people where you'll find something pretty interesting. UW is the stronger program in general though, and if you're interested in academia, I'd take that into account. I think you'll be in good shape for industry at either place. Ann Arbor obviously has worse winters and you're not going to get the ocean and the mountains.
  13. $250k total comp was pretty standard 5 years ago for new-PhD data scientists at top tech companies, so this is not surprising to me, especially if he studied a field like causal inference which Penn is very strong in. Honestly, even at low-ranked PhD programs, you will basically see all of their alums getting jobs at Google/Amazon/FB that will start at $250k+.
  14. I was not aware the UPenn got their students out so quickly. The GSTP at Michigan requires essentially an extra year of genetics courses so you will be busy with coursework for the first 3 years I think, in addition to basically starting out doing research right away. Also the second year statistics theory courses at Michigan are pretty intense, and I think you also have to take PhD genetics courses which are probably difficult for someone who isn't a biologist, but I'm just hypothesizing here. But yes, Michigan's degree takes longer in general and I would take that into account. Even outs
  15. The genetics training grant requires you to take extra genetics coursework that takes some time. All great points to bring up though. I still think the variety of younger faculty like Zhou, Jiang, Wen, and some of their newer hires I'm not familiar with make it an appealing option for someone who wants a lot of options in the statgen world. But of course there are also other factors to consider in making a decision. If @Primadonna is even a little uncertain of whether they want to do statistical genetics, opting for the training grant and Michigan would be a much less appealing option.
×
×
  • Create New...

Important Information

By using this site, you agree to our Terms of Use and Privacy Policy.