Statistical methods used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multiple linear regression, robust regression, logistic regression and classification, bias and variance, and bootstrap method. An important focus of the course is on statistical computing and reproducible statistical analysis. The course includes hands-on experience in analyzing real world data from the social, life, and physical sciences. The R language (or a similar language like Python or Julia) is used.