Course description

Not only the world but also data about the world is becoming increasingly complex. Examples of complex data structures include network data that represent connections among individuals (e.g., friends on social media platforms), spatial data that represents geolocations (e.g., smartphone location data), data collected at multiple levels (e.g., employees in organizations), and text data (e.g., online comments). This course provides students with a comprehensive understanding and set of tools to analyze network, spatial, multilevel, and text data.

- Gain experience in how to prepare, analyze, and visualize data in R and the tidyverse.
- Learn how to extract meaningful and actionable knowledge from network, multilevel, spatial, and text data.
- Do a research project that can serve as the foundation of an honors thesis: learn how to develop a research question, prepare & analyze data, and report & visualize results.
- Lectures on programming and statistics in the morning, hands-on lab sessions in the afternoon.


An introductory class on quantitative data analysis (e.g., elementary probability theory, hypothesis testing, linear regression) and some exposure to programming.

Section ID:SOC 3740 101-SEM
Notes:The SOC 3740 101-SEM section of this course has been cancelled. There may be other sections (listed above) that are open to students.