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.
As I’ve been brainstorming more “advice” posts for this blog, I thought it might be useful for me to write down some thoughts I have about how to read advice. I try to acknowledge on this blog that my perspective is only a subjective, individual one based on my experiences, but I thought I would elaborate a bit more on why that’s important for you to remember as the reader.
I use a Fitbit to track some basic health statistics, but sometimes I wished that the plots on the app were displayed slightly differently. In this post, I will give a go at making plots that I think would be useful for me. To do this, I first need to download my data from Fitbit. One way is to export your data manually from Fitbit’s website. Alternatively, they also have an API.