The Data Without Borders class was described as “Data science in the service of humanity.” It was led by the amazingly talented and enthusiastic Jake Porway, founder of DataKind. The main technical focus of the class was learning to use the software R Studio to analyze data. DataKind connects data analysts and visualizers with not-for-profits that have a lot of data, but don’t know what to do with it.
I took this class in large part because of my Databetes project and an interest in learning about ways of analyzing all the data I am accumulating. Quite obviously, I chose some of my medical data to analyze for the final project. I looked at one month’s worth of blood sugar readings from my Dexcom continuous glucose monitor (CGM). My goal was to look beyond the ways that this data is normally analyzed based on the day’s average blood sugar. Instead I wanted to look at the volatility of the readings. As a patient, I know there are days with great average blood sugar readings but big swings from high to low blood sugars. On those types of days, you definitely don’t feel like you are achieving good control, even though your average daily blood sugar reading says you are. This project gave me the opportunity to explore ways of spotting those days, measure the volatility and creating a metric for determining acceptable/standard versus troubling volatility.