This course teaches a synergetic blend of statistical computing and re-sampling (permutation and bootstrap) methods. Statistical computing allows more flexible investigation of data, such as generating customized visualizations and summarizations or custom-tailoring an analysis. Re-sampling methods can often allow for principled data analysis in circumstances where, for example, the parametric assumptions behind more traditional analyses such as linear regression are held in doubt or the sample sizes are too small for asymptotics to hold. They can also be used when ones estimands and estimators of interest are too complex for theoretical approximations. This course teaches how to program in R, a widely adopted statistical computing platform, and uses case studies and projects to give students hands-on experience. This is an applied course in that the goal is to learn contemporary methods that can immediately be applied to one's own work.