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Hey @footballman2399 what's your source on this? Are you a student there? Can't find anything about funding for MS students on their website!

Choose a different person for the last LOR if possible. Agreed, Magoosh is simply incredible. Shoot for 168+ on Q. Trust me, it's possible Not sure if research experience matters. Think math is probably more important. So yes, still take real analysis as it'll help once in a program. Read the thread below about choosing schools. I strongly agree with @TakeruK. Anecdotally, my first time around (long story) I applied only to reach and match schools in the top 20 and got great results. With no research experience I might add. Of course, YMMV.

Also love hearing from the experts, so if @cyberwulf and/or @biostat_prof would be willing to chime in that would be wonderful.

Bump... I've been looking into this again more recently. Rob Weiss at UCLA suggests focusing on math (https://faculty.biostat.ucla.edu/robweiss/preparation) which is what I'll probably do. But like @compscian said, I agree that gaining research skills is ultimately more important than coursework. I have a family so really need to keep working full-time as long as I can, so I don't have many options to get both the math and the research. So I've found a synchronous/online MS program in Mathematical Sciences housed in a combined Math/Stats department that actually looks very rigorous. It is light on course requirements, only requiring 2 core classes in matrix theory and mathematical statistics, and the rest is pretty much open (just needs advisor approval). They offer a bunch of stuff online on top of the core including real analysis, complex analysis, topology, numerical analysis, advanced probability and inference, statistical modeling, regression, DOE, etc. The best part is they also offer a 2-semester research course as well as a thesis option. Now for the big downside -- it's from a no-name state school. What are your thoughts on how much the prestige of the school would matter in admissions? For my situation, I'm hoping that adding the graduate math coursework plus relevant research experience from any school will put me in a solid position. But hey, I figure an "ensemble" of diverse opinions will perform better on average than just one, right guys?

With a few exceptions, I've been pretty skeptical of the value of masters degrees in data science/analytics/business analytics, favoring a degree in one of the pillar fields like statistics (not applied statistics) or computer science. The main drawback to this approach, of course, is the course requirements in areas that aren't that helpful for data science. However, at the PhD level, since research these days is highly interdisciplinary, especially in domains like machine learning and data visualization, I'm not so sure what to think. There aren't very many such programs yet. Some examples: http://csm.kennesaw.edu/datascience/ https://www.wpi.edu/academics/datascience/phd-program.html http://bas.utk.edu/academic-programs/phd/default.asp Also, I know of another state university currently developing one within the stats department in collaboration with the CS department. I'm very interested in statistics but worry it'd be too theoretical. I'm also interested in biostats but don't necessarily want to be pigeonholed into one application domain (even if just during the PhD). I would actually be most interested in CS if I had taken enough courses in undergrad to meet admission requirements. So I am curious about your thoughts on these types of PhD programs, particularly for someone who wants to work in industry.

Since you included things that won't matter like club involvement and internships but didn't mention your math background, thought I'd let you know that PhD level Finance involves a lot of proof-oriented mathematics. If you haven't taken this kind of math, you should start there. I'm not sure more finance experience, even in research, will help at all without the math.

That's encouraging actually. I took a look at some student profiles and you're absolutely right. In fact what draws me towards biostats is the focus on applications and methods over theory (while still taking a rigorous approach), so I'd much rather go for an MS in stats or biostats instead of pure math. Additionally, I'm sure there are more opportunities to work with truly big data sets and to get exposure to areas like machine learning.

@arima thanks, you make some good points. One thing I forgot to mention – of the ones I've been looking at, i.e. in-state options for financial reasons, the math degree allows for a thesis whereas the other 2 do not. I have a full-time job I love, and again for financial considerations, would almost surely do an MS part-time making it difficult to get research experience otherwise. How important do you think research experience is for biostat phd admissions? Is that a major factor to consider between the degrees? Also, what about factoring in schools like FSU and NC State where biostat is part of the stat department? Would they look down on a stat MS? FWIW in undergrad took linear algebra, calc sequence, diff eq, an intro to proofs type of course, stat theory 1 & 2, and a lot of stat methods courses. Also took grad level stochastic processes and advanced calc while in my first master's. Advanced calc was only 1 semester and was really tough but imagine it was light compared to a real analysis course. So I think I've got the chops for either math or stats. Also, not sure if it would matter for the choice here but my goal after the PhD would be industry.

Thoughts? Statistics - focus on data mining but includes a stat theory sequence (at the level of Casella and Berger I believe) Biostatistics - seems light in terms of theory but gives exposure to public health Math - not applied, mostly theory but includes some computational classes, and has a concentration in where you take a sequence measure theoretic probability and mathematical statistics

It's been noted many times in these forums that most of the Canadian schools offer funded MS degrees in all sorts of disciplines, including Stats and presumably Biostats.

NCSU also has a pretty thorough list, although it's probably not exhaustive: http://analytics.ncsu.edu/?page_id=4184

I'm not in a biostat PhD program, but I imagine at any top 10 program for any quantitative discipline, a quant GRE score >= 90th percentile would be necessary but not sufficient for admission, meaning they probably won't even look at the rest of your great application. See http://www.ets.org/s/gre/pdf/gre_guide_table1a.pdf for scores and their percentiles. I say re-take the GRE. Do everything you can to ace it. I recommend Magoosh. I'm a terrible test taker too and got a 95th percentile score no problem. If you can't, or more importantly, if you are unwilling to put in the time then you would not succeed in a top 10 PhD program anyway...

Agreed, the heavy focus on SAS at NCSU would be a negative from my perspective, too. This day and age it's all about open source tech like R, Python, Hadoop, etc. To clarify, for the Northwestern program, you mean the on campus one in Analytics offered by the College of Engineering? That one looks legit, not as sure about the online MS in Predictive Analytics. BTW, I wouldn't say I have a negative view of bootcamps, more just skeptical that it would be helpful for someone in your position. I could very well be wrong. Try finding and contacting alumni that went in with a similar background as yours. See if they think it helped with placement and prepared them well.

If you really want to be in a technical data science role as you mentioned, then I would recommend excluding business analytics degrees from consideration. Personally, I would do a MS in a more "pure" field like statistics or computer science, if you have the right academic background, and learning the data science specific skills through self learning, MOOCs, Kaggle competitions, and so on. The lone exception is the analytics program at NCSU, only because they're results, which are transparently posted online, speak for themselves. Additionally, the program is only 10 months, which lands you somewhere between a traditional MS and a bootcamp in terms of the time and financial commitment. FWIW my impression is that the bootcamp space seems like it's primarily designed for people with graduate degrees in quantitative areas who want to quickly transition into data science.

Do you mean MSU?