I have experience analyzing various data types, from cleaning data and picking and evaluating model performance (bootstrapping, cross-validation, etc.), to interpreting results and creating visualizations. I have worked with many different types of models, including supervised and unsupervised machine learning techniques, nonparametric and bayesian regressions, generalized additive models, and mixture models. I have used R extensively, as well as Python, SQL, and excel. In addition to statistical modeling, some of my past projects have included using text scraping packages, building an interactive Shiny dashboard in R to display visualizations, and incorporating causal inference techniques like instrumentation and the back-door-criterion into models.