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An introduction to the terminology and concepts necessary to navigate our data-driven world. Students will learn to be critically-thinking consumers of data. Topics include sampling and scope of inference, conditional probabilities, numerical and graphical summaries of data, the basic concepts behind statistical inference, and ethical practice in statistics and data science.

This course will utilize statistical software packages to learn about the fundamentals of data science needed for data analysis. Topics include data acquisition, data cleaning and wrangling, and visualization techniques. Focus will be on the learned techniques as well as on the communication of findings to a general audience.

Intermediate course in applied statistics covering simple linear regression, multiple linear regression (with both quantitative and categorical predictors), and logistic regression. Emphasis is on model fitting, diagnostics, inference, and interpretation. Calculations will be done using statistical software and communication of statistical findings will be a major focus.

Intermediate course in applied statistics focusing on the fundamentals of experimental design and analysis of variance, which allows for the comparison of group means. In addition to the basic terminology and concepts behind experiments, students will learn about common experimental designs and how to analyze them. Such designs include completely randomized designs, factorial designs, randomized block designs, Latin squares, and split plots.