Statistical and Data Sciences 390 - T- ANALYSIS OF SOCIAL NETWORKS

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. : Social networks represent a collection of interconnected people. By using statistical methods to analyze social networks, we can better understand why certain people tend to be connected, how information travels, how to plan better health interventions, and much more. Independence is a common assumption for many statistical tests and models. The interdependence that is inherent in social networks will violate the independence assumption. The course will introduce methods for visualizing, summarizing, sampling, and modeling social networks, while accounting for their complex structure. Concepts and methods will be applied using a variety of network data sets using the statistical software R.