Scientific Reasoning in Spatial Data Science Education

Data science students often have incredible technical skills in computation and statistics but still have trouble critically solving problems with data. We are collaborating with several partners to address this problem through new courses and teaching materials featured here. Students learn to think differently about solving data problems -- they go from more mechanical applications of computation and statistics to using scientific reasoning as the logic for solving spatial data problems - and avoiding common cognitive and statistical pitfalls.

This integration represents a unique opportunity for the social sciences and the humanities to leverage their traditional strengths at UChicago for (spatial) data science education.

More information here about this CSDS priority area, which is led by Julia Koschinsky.