Spring of 2016 I enrolled in my first ever graduate level data science course at the School of Information at UC Berkeley. The course ‘Deconstrucing Data Science’ investigated quantitative methods of machine learning and data analysis. Coming from a humanist background, the course challenged me to think in drastically different ways about evidence, data, and argument. In the process of learning new data science methods, we reflected on experimental design and challenged the underlying assumptions of empirical methods. These critical reflections resonated with similar debates around the ‘scientific’ character of history and the social sciences to draw informed conclusions about the past and society.