DATA 4040: Network Data Science
This course provides an overview of the tools most commonly used to collect, analyze, and visualize network data. For each type of analysis, the underlying theory, assumptions, and mechanics of how each analytical tool works, are discussed, along with appropriate interpretation and visualization of the results. The course is designed for students from any major with real-world examples drawn from the social and behavioral sciences, economics and finance, biology, history, anthropology, social media, public health, and government. All analysis skills will be taught in class.
Course Goals
- Understand the basic nodal and structural properties of networks and how to retrieve their measurement from network data.
- Become proficient in collecting network data, adding nodal attributes, and organizing the data into a proper format for analysis and visualization.
- Summarize the theoretical and methodological traditions that guide contemporary network data science.
- Become skilled at a variety of methodologies for analyzing and interpreting network data.
- Become proficient using open-source software and other technologies for network data collection and analysis.
Students of all skill levels and backgrounds are welcome. It is highly recommended, but not required, that students complete DATA 2020 or an equivalent course in statistics and/or data analysis, preferably one that utilized the statistical computing programs R and RStudio, prior to enrollment.
Ready to take this course?
Search for "DATA 4040" in the Schedule of Classes to register. For more help with registration, please review the resources provided by the Registrar's Office.
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