I recently came across some interesting stories about data that I thought were good examples of how data can be misleading. The first story is this thread on X (Twitter), in which Ben Schmidt critiques a recent tweet/Financial Times article from John Burn-Murdoch.1 John cites and extends analysis from an economics paper that argues that books have begun talking less about “progress and future” and more about “caution, worry, and risk” over the last 60 years based on data from the Google Ngram project.

Happy new year! Today, I thought I’d share a list of books I’ve enjoyed reading in recent years. I last posted about my favorite books in 2019 and I’ve read more books since then, so here are some more of my recommendations. I hope you find something you also enjoy reading from the list. Fiction The Song of Achilles by Madeline Miller - A retelling of Homer’s Iliad, it’s a captivating love story set in Greek mythology.

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.