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. (E): This course introduces concepts and measurement of population health, particularly common approaches to analyze population health using survey data. The course will introduce measures of population health and health disparities. It will also cover common methodological approaches, such as standardization, applied regression modeling, and survey sampling, employed in population health research. Concepts and methods will be applied using large population health data sources, including census data and national health surveys. Prerequisite: SDS 220 or 201 or equivalent.