This course focuses on modern statistical methods for analysis of network data, especially social network data. Networks are characterized by two types of units: Nodes (often representing people), and edges representing the relations between nodes. Network data are characterized by the nesting of edges between nodes, which creates interesting features for statistical dependence and sampling. In addition to statistical methods, the course will include some discussion of connections to motivating problems and underlying social science theory. It will include methods from a variety of statistical paradigms, including likelihood methods, design-based methods, and randomization methods. Most computation will be done using R statistical software. Homework assignments will include theoretical problems , computational problems, and written reading responses. Students will also complete projects of their choosing. Co-requisite: STAT 516 or 608. Recommended: One course in applied statistics (regression STAT 505/697R recommended), Experience in R.