This course is for epidemiologists to learn how to write code. Whether you're a complete beginner or you are switching from Excel or SAS, this course will get you comfortable writing Python to analyze your data. Learning to code will make your data analysis pipeline faster and more reliable, freeing you up to focus on improving the public's health. It will also teach you new ways to think about and interact with your data, making you more effective at uncovering the stories hidden within.
We will cover absolutely everything you need to get started, from installing Python to opening up the software and writing your first line of code. Topics include data visualization, including epidemic curves; risk analyses including odds ratios and relative risk; statistics like regression, t-test and anova; data cleaning and manipulation; and summary and stratified statistics.
Epidemiology began when John Snow went door to door looking for cholera cases in the 18th century. Public health has made a lot of progress since then, but our methods haven't changed much. We can do better! This course is a resource for epidemiologists to learn programming for data analysis, data management, and data sharing.
Questions? Email me at firstname.lastname@example.org, or find me on twitter at @cmyeaton. You can also find the blog and community at episkills.com.
Caitlin Rivers is the author of the package epipy, Python tools for epidemiology. She has a PhD in computational epidemiology and an MPH in infectious disease. She works as an epidemiologist in a public health department.