Data Driven Approaches for Campaigns and

It is strongly recommended that students have completed Quantitative Analysis before taking this course. This class will focus on properly understanding a wide range of tools and techniques involving data and analytics in campaigns. We will study evolutions and revolutions in data-driven politics, including micro-targeting, random controlled trials, and the application of insights from behavioral science, as well as more current approaches using modern statistical techniques, machine learning/AI, and natural language processing/large language models. Our primary focus will be on developments in US political and advocacy campaigns, but we will also examine the uses of these tools in development and other areas. The course is designed to provide an informative but critical overview of an area where it is often difficult to separate hype from expertise. The purpose of the course is to prepare students to understand the strengths and limitations of Big Data and analytics, and to provide concrete and practical knowledge of some of the key tools in use in campaigns and advocacy. Students will be expected to examine the use of data in practical case studies and distinguish between proper and improper uses. The course includes a track to analyze data and will spend more time giving students practical experience with current data and analytic approaches.  Sample code will be provided, and students will be asked to execute and make minor revisions to the code to gain familiarity. Sample R projects will include reading and analyzing polling data, developing predictive models of voter behavior, and analyzing data from social media.  Students will leave with a set of applications that can be customized to work on new data sets.