Throughout my career, I’ve been a data-first researcher, and theory has always been one of my weak areas. This is not to say that I dismiss the importance of theory: I appreciate danah boyd and Kate Crawford’s critique of Chris Anderson’s “the numbers speak for themselves” in their 2012 paper Critical Questions for Big Data as much as I appreciate Catherine D’Ignazio and Lauren Klein’s similar critique in their book Data Feminism.

I have conflicted feelings about productivity books, but even as I increasingly reject the emphasis on productivity, I do find that there are some gems in these books that are helpful to me as I try to keep my life organized across all of its dimensions. While rereading one of these books over the summer, I came across the following quote (which appears to be a misquotation of Oliver Wendell Holmes, Jr.

Reading through the data available through Canvas analytics, I’m reminded of Catherine D’Ignazio and Lauren Klein’s argument (in their excellent book “Data Feminism”) that structured, “clean” data is most necessary for “strangers in the dataset.”

The new semester at the University of Kentucky starts on Monday, and I am flailing to try to get my data science course ready to go—including putting together an open, alternative textbook for my students. I’ve been borrowing heavily from Catherine D’Ignazio and Lauren Klein’s Data Feminism for my textbook: It’s a fantastic resource, and I’m hoping my students take a lot from it. Of course, my kid’s semester has already started, and I’ve already blogged a bunch about my frustrations with her new school’s use of ClassDojo this year.