Lecture 2: Data Types and Rates¶
In this lecture students learn about Python expressions, naming conventions, and core data types. The lecture also introduces incidence rates as a practical application of data computation.
Homework 1: Introduction to Python and Jupyter¶
In this homework students practice Python operations, naming, and functions while applying these skills to real-world public health contexts such as calculating and age-standardizing disease incidence rates.
Discussion 2: Considering Fractions¶
In this discussion students examine fractions as ratios, rates, and tools for measuring population-level phenomena, with a focus on age standardization and disaggregation. Students also reflect critically on the affordances and constraints of different data representations and the social dimensions of measurement.
Lab 2: Python Names¶
In this lab students practice Python assignment, division, casting, and string manipulation while applying these skills to calculate disease incidence rates.