Blog

I recently started working with a set of eviction data for a project on housing precarity at the Urban Displacement Project. As I began exploring the dataset, I was excited to find that it appeared to contain a wealth of historical data we could use to train a robust model for predicting eviction rates in urban neighborhoods. However, my initial excitement soon had to be scaled back when a standard check for missing data revealed that many of the observations lacked values for precisely the variable we aimed to predict.

Machine learning in atmospheric science

A couple years ago, I took an undergraduate biostatistics course here at UC Berkeley and vividly remember one of the first discussion section activities on interpreting data and visualizations. From this activity, I learned about why, as data consumers, we must always be aware of not only what visualizations are really representing but also understanding where the data is really coming from. While this might seem obvious, this has been one of the most valuable lessons as an aspiring data scientist/enthusiast.

As part of my Masters of Public Health program I needed to complete a capstone. Working on a manuscript is a lot of back and forth: You need to make edits, fix your words and figures, and sometimes re-work entire sections. If you are like me, the thought of doing this process over a long period of time in Word makes me nauseous. Two main issues that cause this nausea for me are:

1. I frequently forget to make a record of my writing and often overwrite work