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orchidnora

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  1. orchidnora

    Fall 2019 Statistics Applicant Thread

    In my current department, the specified hours for a TA position are really an upper bound for the amount of time it could take (my teaching duties only take me half the specified time). Hopefully it's the same at NCSU. Somebody else on here probably knows better than I do though
  2. Thanks for the help! I have adjusted my schedule accordingly.
  3. orchidnora

    Fall 2019 Statistics Applicant Thread

    I got an email from NCSU offering unofficial acceptance from the department for the Stats Ph.D. program! Departmental funding was also offered. I also posted this on the results page. Congrats to those of you already admitted, and good luck to everyone still waiting!
  4. Thanks for the responses! The two classes I need to graduate are one math elective and foreign language. The other two classes I signed up for are Foundations of Math (which I’m pretty sure has little relevance to stats) and theoretical differential geometry. I would sign up for research credits, but I have already taken them before. I just don’t want my last semester to be too stressful, since I’ll have no break before grad school (I have a research internship that I’m looking forward to this summer). But I don’t want this to somehow affect admissions.
  5. Hi, everyone! I am graduating with my B.S. in Math and in Stats this Spring, so this is my last semester. I have applied to 7 Statistics Ph.D. programs, all within the 10 top schools (according to US News rankings). Does it matter if I take only 2 classes in my last semester as an undergrad? These are the only ones required to graduate. Other details: Several Ph.D. applications asked me for my Spring 2019 schedule, and on there are I listed 4 classes (2 math classes beyond what is necessary to graduate). This was only on the original applications I submitted in December. I have not received any correspondence from any schools yet. I'm doubtful that my admission would be contingent on the completion of these 2 additional math classes, since I still will have taken 2 years of real analysis before beginning a Ph.D. program. Any free time I'd gain from dropping these 2 classes would be spent working on a research project, which I'm hoping to relate to my Ph.D. research. I would greatly appreciate any thoughts on this, before changing my Spring schedule! Thanks.
  6. Hi there. First of all, I would recommend you repost this to the Math & Statistics Gradcafe forum, since you'll probably get more responses there. In my opinion, participating in more biostats research will only make your more competitive for biostats programs. What programs are you thinking about when referring to "diversity camps"? The 10 week full-time research programs are often NSF funded REUs. One of NSF's goals is to promote diversity in science, so decisions are made in consideration of applicants' race, gender, etc. However, these are legitimate research opportunities. But you should never be losing money from these. The applications should be free, and REUs offer stipends + living expenses ranging from $4000-10,000 value. If you're applying to a research-based Master's program, then research experience is at least equal and probably preferable to internship experience (depending on the nature of the internship). Assuming you already have satisfied prerequisites for a biostats program, then research/internship over the summer is more beneficial than additional coursework in my opinion. Also getting a decent score on the general GRE really should not take months of dedicated study over the summer, if you have excelled in the quantitative courses required for a biostats Master's.
  7. I see, that's an awesome deal! In that case, you may be competitive to apply straight for Ph.D. programs in Stats after these 34 credits, hinging on research experience of course. Do you know what language the stats courses at your school uses? For stats, R will be a big one to know. I wouldn't be too concerned about learning Python specifically, but by all means self-teach yourself in the meantime if you feel motivated. Most likely, you'll get strong in R, Python, or some other language of choice quite naturally through research projects. Now for research, since you say there aren't many research opportunities at your school, you should look elsewhere. I'm not sure if you have other commitments, but assuming not, then summers are prime time for doing research. As I mentioned before, typically it is difficult to get research without taking at least 4 semesters worth of math/stats coursework. However, if you emphasize your past research experience in science (to show that you know how to do research, even though it's a different field) + your independent projects, it may just work. If you can tie in your past Bachelor's degree and the research you've already done to your research interests in ML, then that could work even better for you. Google NSF REUs (there are some in machine learning; sometimes they're listed under stats, data science, math, etc...so just read the description) and national laboratories (Department of Energy, National Institute of Health, etc.). There are probably tons of other opportunities, but those are the ones I know. You could probably start applying for research in Fall 2019 and possibly get accepted to some research program for Summer 2020.
  8. I agree with @Stat PhD Now Postdoc that there are great opportunities in industry for ML from a stats or a CS background. ML is essentially the intersection of math, statistics, and CS. Because of that, you should choose stats vs. CS based on what you like better. Typically, statisticians are more concerned than computer scientists with the mathematical theory underlying ML approaches, including checking assumptions, model validation, model interpretation, etc. In fact, many methods now under the umbrella of ML were originally developed by statisticians. Keep in mind as well that there are Master's in Data Science programs out there. Some are cash cows (so be careful), but others seem reasonable. From what I can tell, they are typically not traditional research-based Master's degrees but rather sort of vocational, in that you learn exactly the skills to be directly applied in industry. A degree like that probably isn't enough to be the head of R&D at Google but can still offer lucrative opportunities in industry. Some of these Master's programs do not require an extensive quantitative background and even appreciate people from alternative backgrounds. Also when I asked about Master's degrees, I meant skipping a second Bachelor's and jumping straight into a Master's. Getting another Bachelor's involves repeating a lot of unnecessary coursework. I believe any Bachelor's should take more than 3-4 semesters. Are your previous credits counting toward it? Or do you mean that you plan to take 3-4 semesters of coursework but will not earn a Bachelor's from it?
  9. Hi there. First of all, is there a reason why you want to jump straight into a Ph.D.? Given your background, it might be more efficient to complete a Master's in CS/Stats/related fields. I have heard that some of these programs like having applicants from industry and other academic backgrounds. Then if you still want the Ph.D., you will be in a much better situation to apply for top programs. If you do prefer to get another Bachelor's, I can offer some advice on that. I don't think that being out of school for three years will significantly affect your chances in a negative way. But I think you will have to gain some research experience, like all applicants (traditional background or not), before being admitted to top Ph.D. programs. Moreover, it would be good to ensure that you like research, before committing to a five year Ph.D. program. It's not necessarily a problem that your school has limited research opportunities, since you can find research opportunities elsewhere (you can apply for summer NSF-REUs, government internships through NIH, U.S. Census Bureau, DOE labs, etc., although it might be difficult to get these positions until after more than 2 semesters of coursework...). But keep in mind that pursuing a Ph.D. typically means a willingness to move anywhere for the best research and career opportunities, so staying in NYC might restrict your options. Also if your school has limited ongoing research in general (meaning that there are few researching professors and graduate students) then you may consider attending a better school. Unfortunately, the rankings of an undergraduate institution do matter to some extent in graduate applications (Bs at MIT probably count more than As at an unknown school).
  10. Typically, for top stats Ph.D. programs, it’s best to take as many theoretical math courses as possible. Generically, functional analysis might look best if you can get a good grade. However, if you want something directly applicable to applied statistical research, I would recommend taking nonparametric stats. An at least basic understanding of these methods is necessary imo for many data analysis projects. Of course, if you don’t take it now, you’ll probably end up seeing it in grad school anyway. On the other hand, if you really enjoyed Stochastic Process I and might wanted to study it in grad school, then take Stochastic Process II. Really, it’s up to you: how you want to spend your semester and what you intend to do in grad school. When applying you’ll have the SOP to emphasize any strengths in coursework, so think about what you want your coursework to represent about you.
  11. Thank you for the info! This is very reassuring. It seems my recommendations will also only be a little late (less than one day late). At this point, it's out of my control, since I already sent a reminder and don't want to harass my recommender. I will probably call admissions on Monday just for sanity.
  12. It's so stressful! And for some deadlines, I think you're right. But others are different, for example, Harvard is due 5 P.M. Eastern today.
  13. Unfortunately, my recommenders have missed the December 1st (November 30th 11:59 P.M.) deadlines. Anybody else nervously awaiting their letters? Hopefully this won't affect anyone's chances of acceptance....
  14. Thanks, ya'all! In that case, I will upload the papers as supplementary documents, keeping in mind they probably will not be fully read. Hopefully my recommenders have discussed my contributions to the work.
  15. Hi, everyone! I have some papers already available online (and I provided the links to them in my CV submitted for graduate applications). Is it a good idea to also upload the papers as supplementary documents in grad school applications? I'm concerned that application reviewers may not notice the provided links, while the papers are hard to miss when submitted as supplemental documents. I'd love to hear anybody's thoughts! Thanks.
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