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bayessays

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bayessays last won the day on May 10

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About bayessays

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  1. I don't think I've ever seen a single program that required a calculus-based statistics course. As @Stat Assistant Professor said, many students have little stats background and most programs don't require any statistics courses. You're good, don't worry about this at all.
  2. Yeah, I don't think the early grades will be a huge issue given the clear ability to do higher level coursework at a top school. I still think UW and Michigan are reaches in the sense that they are very competitive and you are not guaranteed admission, but you should definitely apply to schools at that level. UCLA and UCI are great programs that I think you should probably get into. (Honestly, I think UCI and UCLA are pretty comparable in terms of program quality - UCI is a very strong department). You don't need to be applying to programs like UCSB, MSU, Maryland unless you have personal reasons for wanting to attend those schools. I would add more schools in that 10-30 range.
  3. My personal inclination would be to take the upper level stat theory unless you really like analysis, but I don't think there's a wrong answer.
  4. I mean, if you have a near-4.0 from a top 3 biostatistics program, I think it is probably not a waste to apply to any other biostatistics program. But obviously the programs are competitive so it is not guaranteed either. I'm not sure how much it will matter, but is your B in a theory or applied class? If it's in stat theory, I could see a B hurting if you don't have more advanced classes. I don't imagine the applied classes matter as much, and a 3.96 is great anyways. A lot will probably depend on your letters, so I'd apply to a wider range as well.
  5. That totally makes sense. I'm not super familiar with master's programs and am not really sure how competitive the top ones are. I know Michigan is not super competitive, but I think Harvard/JHU might be a stretch. Look through the https://www.thegradcafe.com/survey page at the programs you're interested and look at the stats of the rejected American MS students.
  6. From what I've seen on this forum, Boston U is way more competitive than its ranking suggests. I've seen people who got into top 5 programs and then rejected at Boston. Same for Pitt. I think the other 3 are reasonable for sure though. As the above poster said, if there's any way you could get your Q score up a couple points I think it could really help.
  7. For biostat PhDs, your GRE Q is a little lower than ideal but not disqualifying, and it would be better if you had real analysis but you otherwise have a decent math background if you have A/A-s in those classes. From experience on this board and in real life, I do think under-represented minority status can help you out if that's the case for you. I think your exact grades in those math classes and whether you are a URM will give a clearer picture of what range of schools to apply for. I think you should apply for some biostat PhDs outside the top 10 (ie, outside the top 40 on US News). Do you have any particular schools in mind? As for whether you should do biostat or epidemiology, that really depends on what you're interested in. Epidemiology is much more specialized, and most epidemiologists don't specialize in statistics and finish graduate school with about the amount of statistics training that you already have. You can be a professor in a tougher job market (fewer public health schools) or become a public health professsional. Biostat will leave a lot more career options - professor, pharma, tech, finance, anything that involves numbers really. If you enjoy math and are undecided, biostat is probably the safer and more lucrative career option.
  8. Yeah, good catch, I didn't notice that that wasn't a dedicated linear algebra course. Was the MS entirely online too? Since you want to do more applied work anyways and thus probably don't want to be a professor, department rank matters much less. Those schools I listed might be on the higher end of where you could conceivably get in. There are plenty of lower-ranked biostatistics programs too - take a look at the US News list. Again, a few more math classes would significantly strengthen your application.
  9. The biggest thing you're missing is a real analysis class. It's not 100% necessary for lower ranked programs, but certainly helps. Make sure to do very well on the GRE Q. I would recommend looking at lower ranked biostatistics programs. They will be more lenient about math courses and you can do more applied research (including things that are related to environmental science or epidemiology/social science). I'd start your search at schools like Iowa, VCU, Medical College of Wisconsin, etc.
  10. You should apply anywhere you want, I'm sure you'll get into some top ten PhD programs.
  11. As long as you can get some good recommendations, I think biostat departments will appreciate the unique background and experience and you'd get into some solid PhD programs as is, especially after adding real analysis and doing well.
  12. What subject do you want to get a PhD in? These are professional degrees so they don't clearly lead to a PhD in any subject. If you plan to get a PhD in statistics, you should just take the required math classes and apply.
  13. If you want to do data science, I would recommend leveraging your current degree into an analytics role and self studying some Python on the side.
  14. I can't offer a lot of advice on master's programs, but if you already have a graduate degree in analytics, why spend all that money on a second master's?
  15. Some people on this forum seem to know people who do things like this, but I'll just give my experience/knowledge with tech companies. If you're doing deep learning or something more CS-related, there are probably many more such opportunities. The ML/Statistics research group at Microsoft has 9 people, and many of them are not statisticians: https://www.microsoft.com/en-us/research/theme/machine-learning-statistics/. Google to my knowledge does not have statistics researchers outside of their normal data scientists (who do things that you might consider research?). Facebook research does seem to have a bigger core data science team, which is more research-oriented. The biggest key to being hired is likely having done the right type of research (deep learning, causal inference, networks, something that they have a need for) and having impressive publications. The compensation is going to blow away anything in academia. Entry level data scientists with PhDs start at these companies at $200k+ total compensation. I imagine researchers make at least that. It is my impression that there are not enough of these positions for it to be a definite career path. There are hundreds of people with PhDs from top 20 statistics programs just working as regular data scientists at these companies.
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