I use a note-taking app at work called Obsidian, but only recently did I get around to learning how to use more of its functionality. For the uninitiated, Obsidian is a note-taking app that uses markdown, which is a markup language that’s simple to learn and readable.1 The advantage of a markup language is that it makes your formatting explicit in what you write. If you’ve ever struggled with manually adjusting unexpected indentations, spacings, etc.

Since graduating from my PhD program in 2022, I’ve been working in the tech industry as a data scientist. This transition has given me more perspective on how the research community is spread across academia and industry. One interesting thing I’ve learned is that the boundaries are blurrier than I had previously thought. It’s true that when I was a PhD student in the biostatistics department at JHU, I was already aware that there were professors who had collaborations outside academia.

Recently, I’ve been working on revisions for a paper1 from my PhD and it reminded me of a valuable lesson I learned while working on some tricky statistical analysis for that project. The idea is what I think of as “debugging” your analysis.2 Debugging in a programming context refers to the process of identifying and fixing bugs (errors in your code). But a similar concept of “debugging” also applies to data science.

As a biostatistics PhD alumnus who just graduated earlier this year, I thought I would share some of the things I learned regarding potential career paths. If you are a current PhD student, I would encourage you to seriously think about your future career plans in your third or fourth year, with some level of detail beyond “I want to stay in academia” or “I want to go into industry.

This post is sort of a reflection on my PhD experience, condensed as responses to some major misconceptions I had about a PhD (or misconceptions I’ve heard other people express). I hope you find it to be a useful, or at least interesting, read. Misconception #1: If you enjoy your field and want to study it at a deeper level, you should get a PhD. It’s not exactly that this isn’t true, but I think it’s not an accurate representation of the purpose of your PhD program.