Foundations of Data Science in Astronomy credit: 3 or

4 Hours

Introduction to statistical and data science methods used in modern astronomy. Topics include robust statistics, regression, model building and hypotheses testing, MCMC methods, parameter estimation, time series analysis, clustering and dimensionality reduction, and hierarchical modeling. Applications to astronomical data are emphasized, as well as best practices for writing code and version control. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: ASTR 210 , ASTR 310 . Prior coursework in linear algebra, e.g. MATH 227 or MATH 257 , recommended.