Spring semesters in the past, but it has now moved the fall semester. This course assumes that students have a solid grounding in basic statistics, including linear regression, ANOVA, and similar methods. It covers a range of semi-parametric, non-linear, and data mining techniques. The focus on establishing a rigorous, theory-grounded approach for data analysis, especially for developing strong predictive models. Students complete an in-depth project in which they analyze a data set, preferably from their own research. A number of these papers have been published as peer-reviewed journal articles. The course uses the R statistical language.
Prereqs: EN.550.420 AND EN.550.430 or equivalent.