This chapter introduces the goals and methodologies of social network analysis (SNA). Unlike traditional data mining approaches that infer connections, SNA directly examines explicitly linked datasets. For instance, social media users are connected through friendship links, researchers are connected through joint publications, and attraction points are connected through tourist co-visitations. The chapter explores core terminologies like nodes, edges, cliques, and centrality measures, which help quantify network connectivity, influence, and modularity. It also introduces visualization techniques and tools like Gephi and NodeXL, which facilitate network exploration and analysis. The Python lab provides a practical example of analysis of a unipartite collaborative network of tourism scientists and a bipartite network of tourists/attractions co-visitations in St. Augustine, Florida.