Most of what social scientists, policy analysts, and industry researchers know comes from observational data, information generated by the world rather than by experiment. This course trains students to work with such data credibly. Students learn to manage and clean real datasets, to build and interpret multivariate regression models, and to present quantitative evidence clearly in figures and tables. Throughout, the emphasis is on translation: how a theoretical claim about politics or society becomes a testable specification, what that specification can and cannot establish, and where the gap between association and explanation opens up. Alongside building their own analyses, students evaluate, replicate, and critique published social science research, developing the judgment to recognize when a finding is well supported and when it is not. By the end of the course, students will be able to take a question from theory to data to a defensible empirical result and be fluent users of R.