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Learn fundamental concepts of social network analysis through hands-on experimentation with R programming in this comprehensive course from Duke University. Master the basics of network layouts and data structures while progressing through seven practical R labs that guide you from creating your first social network graph to implementing advanced analytical techniques. Explore essential network design principles and improve graph readability before diving into positional features and clustering methods. Develop expertise in key centrality measures including in-degree, closeness, betweenness, and eigenvector centrality through dedicated laboratory exercises. Calculate important network metrics such as density, average path distance, and degree distribution while applying clustering algorithms and centrality analyses to real network data. Conclude by examining how surveys and storytelling techniques enhance social network research, gaining practical skills to design, analyze, and interpret social networks using R's powerful analytical capabilities.
