Data Science Applications in Politics and Policy
This course introduces students to the latest empirical research on the applications of novel, large-scale, and unstructured data in political science and public policy. Themes include causality and credibility; administrative and open data; geographic and geospatial data; social media and digital trace data; text, image, audio, and video data; and generative Artificial Intelligence (AI). For each type of data, students learn the set of questions it can help answer, along with appropriate methods for analysis. The course situates the rise of data-intensive social science within the broader context of political science and policy research and examines the promises and pitfalls of computational innovations and new data science methods, including their implications for ethics, transparency, and privacy.
Topics
- Critically assess the application of data science methodologies in political science and public policy research.
- Apply computational methods to novel, unstructured, or large-scale data sources to address political science and policy questions.
- Evaluate the practice and challenges of producing credible evidence for public policy, including the importance of replication and reproducibility.
- Replicate the data analysis in published research or policy papers using appropriate methodologies.
- Explain the opportunities and limitations of new data sources and tools, including generative AI.
Official roster learning outcomes
Prerequisites & course information
PUBPOL 3100 or PUBPOL 5610 or PUBPOL 5310.
Sources
- Course catalogue ↗2026-2027