This course is focused on the fundamental elements of data analysis in the fields of ecology and evolutionary biology. Students will learn how to interpret and model biological data with modern methods for estimation and inference using the R computing language. Topics include: introduction to R and Rmarkdown, data plotting, navigating errors/getting help, introduction to probability theory, demystifying probability distributions, introduction to deterministic relationships, likelihood-based inference, Bayesianism vs. Frequentism, and modes of inference.

This course satisfies the Quantitative Analysis II requirement for a number of Program in Biology majors. Please refer to your major program requirements or meet with a Program in Biology advisor to determine how the course will work for you.

Course Goals

1. Provide the necessary background and quantitative foundation to learn how to analyze biological data

2. Introduce R, a powerful programming environment for data analysis and presentation. Develop skills at writing R functions, with the goal of being able to perform advanced, computationally intensive analyses

3. Provide a conceptual introduction to model-based analysis

4. Lay the foundation for developing a sufficient and appropriate background to teach yourself new methods for data analysis as needed

Course Requirements:

The course assignments will consist of class participation, weekly problem sets, and group projects/presentations. Students will have the opportunity to work alone as well as in teams.

Class Format:

2 x 1.5 hr/wk lectures with in-class work.