PhD students must master a variety of non-statistical skills, and these skills are often acquired through osmosis or trial-and-error. To speed the process along, I have compiled some advice on such topics based on my personal experience. These pages are exclusively my own opinions, and are far from comprehensive. I’d like to add more topics, so please let me know if there’s something of this sort that would be helpful to you.

Along similar lines, Aaditya Ramdas (Assistant Professor at CMU) has also compiled several “checklists” for stat/ML PhD students that I find quite helpful.