biostat_student

I got an offer from Northwestern and can confirm that they do have great placements (at least in terms of industry). Just the Northwestern name itself is very helpful. Both the students and staff were very friendly on my visit. I can't advise whether you should apply or not, but here were my reasons for not going (all are based on personal preferences): Very small PhD cohort Department building is basically a big house instead of its own building Coursework seemed rather traditional (e.g. Survey Methods being a required course that gets covered in the qualifying examination) All the above completely depends on your preferences and end goals. Honestly they could all even be advantages depending on what you're looking for. These were just my observations. I still think its a great department, wonderful location (both life-wise and for career opportunities), and were very accommodating and personalized. If you do apply and get in, they offer a first-year fellowship to some students which you should ask about. Good luck with the process!

Thanks for the advice @Stat PhD Now Postdoc! Were there any specific resources you used that helped you throughout your reading, or was it more just googling individual topics when you needed further explanation? Were you able to skip around based on topics of interest, or was it better to read sequentially?

I'm planning on self-studying Elements of Statistical Learning (by Hastie, Tibshirani, and Friedman) over the summer. It comes highly recommended by a couple students in my program and current lab (which has a major machine learning component). I'm currently a Biostats PhD student who has finished their first year of graduate coursework (Probability and Statistical Inference I/II, Statistical Methods, Intermediate Linear Models). My goal is to read most of ESL, and if that turns out to be too aggressive of a goal, then parts of it, in order to understand the theory behind a variety of machine learning methods. I don't intend on understanding every word, but I want it to be something useful to me as I move forward into research, and something that I can keep referring back to when needed. I've read from many sources online that ESL is an incredible textbook, but some sections are difficult to understand even with a decent math/stats background. I'd love to hear any advice that people who have read the book (or attempted) may have. I don't completely know what I'm in for but want to get the most out of it, because it will be a huge benefit to me throughout my PhD program. I'm especially interested in the following chapters: Ch 3 (Linear Methods for Regression), Ch 4 (Linear Methods for Classification), Ch 7 (Model Assessment), Ch 8 (Model Inference and Averaging), Ch 9 (Additive Models, Trees, and Related Methods), possibly Ch 11 (Neural Nets), Ch 12 (Support Vector Machines), Ch 14 (Unsupervised Learning), and especially Ch 18 (High Dimensional Problems). I'm open to hearing any thoughts or recommendations, whether general or specific. Here are some questions I had: How readable is this book for a someone who has competed the first year of graduate statistics courses (plus math undergrad)? What background topics/subjects were particularly useful to have brushed up on before starting to read? Were there any supplements/resources you found useful? What are some methods for effective reading of the book? (balance between reading/doing problems, setting goals, etc.) How much does each chapter rely on the last? Can you skip around or do you need to go sequentially? Understanding that the following are very subjective: What sections did you find valuable? What sections did you gloss over? Were any sections more difficult to read than others? Chapters that I haven't listed above that are important? Chapters I've listed above that you didn't find as important? General tips for getting the most out of the book or advice for someone before they start reading.

Anyone know when the new 2019 USNWR rankings of graduate Statistics programs are supposed to be up??

I wanted to second the above message. As as current PhD student in the department, I can tell you that the claim that "students don't really choose their advisor" is completely untrue. Everyone I have met has picked his/her advisor, and the vast majority of students I've interacted with thoroughly enjoy their research and has a positive relationship with their advisor. Additionally, almost all admitted domestic students are guaranteed funding, and if you have already received confirmation of funding yourself, the claims about non-guaranteed funding are irrelevant. Apart from that discussion, a positive about UNC is that many more students are funded through GRA's than teaching assistantships. This allows for much more exposure to research and programming skills in early years rather than teaching obligations. Additionally, though research assistantships across PhD programs technically expect work for up to 20/hrs a week, this is rarely true at UNC except for specific cases. Not guaranteed, but in your first and second year, you can generally expect to have to work no more than a couple hours a week. Most students who do more than that do it not because of requirement, but through their own desire to expose themselves to research practices early. Thus, UNC offers the opportunity to get involved in projects at an early stage, while giving students the opportunity to focus more on coursework early on if they so desire. I don't know about your interest in data science specifically, but UNC Biostats has also revamped its data science curriculum over the last year, in order to provide strong data science training as part of the PhD. Helpful especially from an industry recruitment perspective. All first-year students now take a revamped introductory data science course (course website) which is updated yearly to include modern methods and keep up with industry practices. Additionally, you can follow up that course by taking Statistical Computing (also a revamped course so message me for the updated syllabus) and PhD electives that build on this knowledge, such as BIOS740 (Precision Medicine). Again, don't know about your interest in machine learning, but UNC Bios houses a lot of UNC's overall expertise in machine learning, perhaps as much as the computer science department itself. Along with the revamped data science focus, this department is one of the strongest theoretical departments within biostatistics, which bodes well for academic prospects. You'll work hard, but the program is well-balanced between theory/application and built to set up both academic and industrial prospects.