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Master's at Michigan or UC Irvine


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Hey everyone,

Alright, I've already posted a few questions about each of these schools, but I am really trying to decide between these two programs. Any help would be appreciated! So far, I've been accepted to Michigan, UCI, and UC Davis. Davis doesn't seem to offer Bayesian classes, which is a big concern for me. So I've narrowed it down to the other two (assuming the schools I haven't heard back from will ultimately reject me). Here are the positives for each program that I can see:

Michigan

+ Highly ranked program

+ High possibility of some funding

+ More faculty members with wide ranging interests

+ Less crowded place, cost of living is lower

- Climate is not the best

- Very far away from any family/friends

- If I don't get any funding (which wouldn't be announced until August), it will cost quite a bit more than UCI

UC Irvine

+ Smaller class size=more interactions with professors

+ The department char is going to be the president-elect of the American Statistical Association in 2016

+ Very Bayesian-focused (which is direction I think I want to go)

+ Climate is wonderful—an important factor as my mood often follows the seasons

+ I know people in the area, it's a one-day drive from home, and I would have plenty of visitors given it's southern California

- The program didn't exist until 2005, so it's still proving itself

- The program isn't ranked by any major publication (probably due to the previous point)

- High cost of living, crowded

- Most likely will not receive any funding (although, the chair mentioned some reader positions)

Does anyone have experience with either program (particularly Irvine since it is newer)? I'm not exactly sure what I want to do once I graduate. There is a chance I will want to pursue a PhD, but I could also go straight into the workforce.

Thanks in advance!

Edited by Lucky815
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Personally, I don't think either would be good preparation for a PhD program. Michigan's program is applied statistics, which I personally feel would not make you any more competitive for PhD admissions should you decide to go that route. Also, while it's probably much cheaper than Irvine, Ann Arbor is by no means cheap. As far as UCI goes, I don't really know anything about either their masters or PhD program.

 

If you think you'd like to go to industry, I would choose UMich. If you think you want to do a PhD, I'm not really sure which one would be better. Frankly, I think that UC Davis would actually provide you with the best situation--a school with a reputable PhD program and it's in California.

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Thanks, footballman. Well, Davis doesn't seem to offer much in the way of Bayesian statistics, which is the route I really want to take. Right now, I would say that I don't want to get a PhD, but I'd like to keep the option open. Basically, I want to know whether choosing UCI over Michigan would be a "bad" decision. I'm trying not to choose a program based on rankings, and I'm still perplexed that Irvine doesn't appear anywhere. I figure it's because the program is so new, but maybe it just isn't a good program...

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Well, Davis doesn't seem to offer much in the way of Bayesian statistics, which is the route I really want to take.

According to the graduate handbook and the MS website, you can take STA 145 (Bayesian Statistical Inference) at UC Davis as an elective course for credit towards the degree. Looking at UMich's requirements, you can take only one of STATS 414 (Introduction to Bayesian Data Analysis) or BIOSTAT 682 (Applied Bayesian Inference). UC Irvine appears to offer two courses in Bayesian statistics. According to their catalogue, you may take 5 electives, so I guess you could take both courses.

 

Still, I am not convinced that going to a lower ranked school is worth taking one extra course in Bayesian statistics. Most people on this forum seem to believe that school reputation does not matter for going into industry. I personally am not one of these people.

 

I suggest that you email UC Irvine and ask them about where they've placed people. Hopefully they've kept such data. If they have not, it's a red flag.

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That's probably a good call. I just sent over an email to the department chair. I'll see what she says. I know she used to be a professor at UC Davis but left in 2008. At this point, I really just wish I could get an acceptance letter from UCLA or UW. If I did, it probably wouldn't be until after the 15th, and I don't know if it's kosher to rescind an acceptance to go to a better program...

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Irvine has great professors; if you do well and are able to get good letters from them, that would hold a lot of weight when applying to PhD programs.  They aren't ranked because they are a new program; one of their PhD graduates just got a job at Minnesota.  Michigan's program is very applied, which may not help much when applying for PhD programs, but there are still a lot of opportunities to take more theoretical classes as electives.

 

If you really go the extra mile to take advantages of the opportunities at Michigan and are a top student, I could see it possibly being a slightly better launching pad for a PhD.  But it sounds like you want to go to Irvine, and I really don't think you can go wrong there, especially if you want to be in California long-term.  I suppose your decision also depends on how much of a risk you want to take on whether Michigan will fund you.

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I think the choice might depend on your background and the amount of flexibility the programs give you to customize your degree. Have your experiences so far better prepared you for industry or academia? What do you need to supplement to improve your chances for each route? For example, demonstrating strength in real analysis is valuable for PhD applications whereas an applied regression class probably won't mean much. But for the jobs that MS Stats students take after graduating, no one really cares whether or not you know real analysis. They want to see demonstrable data analysis and programming skills.

 

You might want to map out the course of master's level study that you think best fits your goals and see which program better fits that. You can also ask people at the two programs what their master's students have gone on to do or whether the degree studies have some flexibility. In my experience, if you have a coherent goal and know what you are doing, your program will work with you to help you achieve that goal. Then you can judge which program better fits your needs.

 

I wouldn't let the Bayesian offerings influence your decision very much. Every program is going to have some appropriate Bayesian course(s) at the master's level, and master's programs generally aren't intended to be very specialized anyway. It might be helpful to go to a more Bayesian school to confirm your interest in Bayesian research topics, but If you are interested in specializing in some aspect of Bayesian statistics, your PhD studies would be the time to really dig into that, and your decision of where to apply/attend for a PhD would be heavily influenced by each program's Bayesian faculty. Besides, students beginning their master's studies usually don't have a defined research interest beyond maybe some general ideas because there are many areas of statistics they have no exposure to, and even those who do have interests often see them change.

 

Either way, I think that no matter which of those three programs you chose to attend, as long as you did well, took advantage of the opportunities present, made connections, etc, you could be competitive for both high quality PhD programs and good jobs.

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