Lecture, two hours; laboratory, two hours. Requisites: Computer Science 31 or Program in Computing 10A; Life Sciences 30A and 30B, or Mathematics 3A, 3B, and 3C, or 31A, 31B, and 32A; Life Sciences 40 or Psychology 100A or Statistics 10 or 13. Modern statistics and data science study that builds competency in computational data modeling and analysis. Designed for Computational and Systems Biology majors. Interdisciplinary topics integrate data-driven statistical and computational modeling, and resampling methods for understanding and modeling data. Key topics include advanced data visualization, simple linear regression, clustering, classification, and dimensionality reduction techniques, the same principles and techniques that are the building blocks of machine learning. Study is modular, with opportunities for peer collaboration and proposal writing components. Acquired knowledge and skills may immediately be applied in research or industry settings. P/NP or letter grading.