Lecture 5: Variables¶
In this lecture students learn how to operationalize concepts into variables, exploring different variable types and the unit of analysis. The lecture also covers the processes of aggregation and disaggregation as tools for working with data at different levels.
Lecture 6: Variables II¶
In this lecture students build on their understanding of variables by examining the relationships between samples and populations, and distinguishing between association, causality, and confounding factors.
Lab 4: SF Food Safety¶
In this lab students apply their data skills to a real-world San Francisco food safety dataset, working with missing data, outliers, filters, and variable types to investigate data collection and unit of analysis.