Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining. This course may be repeated for credit with different topics. Prerequisites: MTH/SDS 290 or MTH/SDS 291 or MTH/SDS 292. : This course introduces students to applied data analysis using Structural Equation Modeling (SEM), a multivariate statistical approach that allows for simultaneous estimation of coefficients from multiple, related, linear models. With SEM, complex theories can be tested wherein a construct is a response variable, but also is a predictor of another construct. Students in this course will develop a fundamental understanding of strategies for model specification, identification, estimation, and determining model fit. Topics will include factor analysis, latent variable modeling, mediation analysis, measurement invariance testing, and latent growth curve modeling. Emphasis will be placed on the practical applications of SEM and latent variable techniques to address relevant questions in psychology, education, government, and the social sciences more broadly. We will use R computing software.