bernoulli_babe

For statistics / biostatistics PhD programs, how important are research interests? Should I directly reach out to professors concerning research alignment? For example, I've been working extensively with EHR data and Bayesian statistics and have a few professors I'd like to work with. I spent hours researching programs and people that would be the best fit. My question is how important this research alignment is in the application process? On a side note, how important are poster papers in the application cycle? I've worked on 5+ posters so far and am working on 2-3 papers that'll hopefully get published. I only have 3 papers to my name and they're not nearly as theoretical. If you can could you list programs where reaching out to individual professors would have a marginal effect? My concern is that I have the weakest math background (CS major) among my peers but I'd love a stats or more theoretical biostats program since I'd prefer proving error bounds etc.

As someone in industry, an MS is more than enough to get a job as a data scientist or biostatistician. If you're still hesitant about doing research then I highly recommend you go the MS route first then decide after working whether you want to pursue a PhD. I personally don't think a PhD is worth it for industry career growth. EDIT: I'd like to add that if you're looking for a PhD program than I'd look into theoretical MS Stats programs. If you're looking for industry then go to well known CS programs that have MS Data Science. Tech companies will be recruiting from there. From what my friends have experienced, the MS Data Science programs are a lot more applied than most MS stats programs.

@MathStat @Stat Assistant Professor Thanks for your replies! I get the sense that a good number of students really struggle at certain programs or that they are overworked with homework. Some places seem to have grueling qualifying exams like UW. I read somewhere that if you struggle with Casella Berger then you're not well-equipped to do a PhD at a top program. I personally thought that Casella Berger was manageable but not easy. For example, can a biology or CS major complete a top biostats or stats program granted we've had limited exposure to Real Analysis or Measure Theory?

Which programs have grueling coursework for early years? I'd say Casella Berger is a solid read and manageable. Would Casella Berger be too easy for these programs? I was looking through a few programs and these were my thoughts: Hard: Berkeley (Stats), Stanford, UChicago (Stats), Duke (Stats) , UNC (Bios) , CMU, UW (Bios) Manageable: Harvard (stats + bios), Berkeley (Bios), Michigan (stats + bios)