This course on Social Network Analysis (SNA) equips participants with a thorough understanding of SNA principles and applications, tailored for individuals in the social sciences such as researchers, students, and professionals. It weaves together essential theoretical concepts with hands-on practical applications, placing a spotlight on the effective visualization and analysis of network data.

The curriculum introduces participants to the core ideas of SNA, emphasizing the significance of network structures and key measures like degree, betweenness, closeness, and eigenvector centrality. The course highlights SNA’s utility in revealing the complex networks of relationships that shape social structures and influence behavior.

Progressing through the course, participants will explore advanced network measures and community detection algorithms while integrating SNA with computational data analysis techniques. This dual approach enriches the learning experience, enhancing participants’ skills in formulating intricate research hypotheses and managing diverse datasets. Such skills are pivotal in fostering a deep understanding of network dynamics across different contexts.

A hands-on workshop component allows participants to directly apply the concepts covered by working with provided datasets. Focus areas include centrality measures, modularity, and effective visualization techniques. This segment is specifically designed to gear participants up for their own SNA projects, initiating valuable discussions on research design, tackling potential challenges, and identifying additional learning resources.

Upon completing this course, participants will possess a solid theoretical foundation in SNA, alongside the practical skills necessary for conducting detailed investigations into social structures and dynamics. This knowledge base prepares them to navigate their research or professional pursuits with a higher level of competence and insight.