Connections and flows across the world have become increasingly important for knowledge/information sharing, illicit activities coordination, trade, and the spread of misinformation and contagious diseases. Social network analysis (SNA), not to be confused with social networking, is a specialized methodology that examines the patterns of relationships among individuals, community, countries, etc. SNA uses visualization to identify how networks are structured, who the most important or influential people are in a network, social capital, sub-groups, and if time permits, "hidden or shadow networks". SNA can also be used to evaluate collaboration, coalition, and partnerships. In this course, students will gain an overview of the theories and methods of social networks including collecting and analyzing network data. Topics that will be explored include: network structures, network position and performance, peer effects, network formation, and network activation-maintenance-disruption. We will draw on examples from development, public health, criminal justice, and social media. While no prior statistical knowledge will be assumed, a familiarity with using Excel is essential for success in the course. For those who are not very familiar with Microsoft Excel, they will be required to complete an online (free) training course in the first two weeks of the course. Success in the course will largely depend on students' curiosity , patience, and not being afraid to look at the world through a different and potentially unfamiliar analytic lens. Classroom sessions include lectures, discussions, and technical sessions. Course open to IDCE graduate students; ID seniors or ADP students with previous analytics experience. If space permits, graduate students from other departments may request permission to enroll in the class. Concentrations: Monitoring Evaluation, and Learning- Health Equity- Refugees, Forced Migration, and Belonging.