Through lectures, in-class activities, and problem sets, students learn and use methods in data science to execute a project focused on a Quantitative Structure Property Relationship (QSPR). Students complete a typical research-based data science pipeline, including project definition, metric evaluation, data collection, data cleaning, exploratory data analysis, model selection, visualization, and reporting. During data cleaning and exploratory data analysis, students learn key concepts about univariate and multivariate statistics. Throughout the project, students learn about bias and fairness, the reproducibility crisis, statistical paradoxes, and more. Python is the programming language of instruction.