Social Network Analysis for Social Science (SNA) is the second part of the data analysis track for economics and business.
This course is designed to introduce social network theory and methods to students. SNA is dynamic and interdisciplinary. It is employed by economists who study social embeddedness –a degree to which economic activities are constrained by social ties. Individuals who occupy better positions in social networks or have access to useful social ties are more likely to get higher rewards from economic transactions. Students will study how to collect data on social networks, study their structures and investigate positions of people in networks. Moreover, students will learn how to employ these data to predict economic outcomes such as having a better job or higher wages. Moreover, students will be introduced to such concepts of social science as general social trust, social capital, signaling theory and the tragedy of the commons. In terms of SNA, students will learn such concepts as homophily, small worlds, and preferential attachment. All these concepts are highly relevant in social science as well as natural science and studies of communication.
Social network analysis requires certain skills of data analysis: (1) where and how to get data on networks? (2) how to analyze network data? (3) how to visualize network data? Studentswill learn such techniques as “name generator”, they will study such statistical models as QAPcorrelations (Quadratic Assignment Procedure) and Exponential Random Graphs Models. Students will learn how to use ggplot2 in R to draw graphs.
Moreover, students will learn basic R and basic Python in order to scrape data online, create data frames and transform them into matrixes which are building blocks of graphs.