Spring 2024 Political Analytics PS5120 section 001

Electoral Data & Predictive Modeling

Electoral Data & Predict

Call Number 12640
Day & Time
Location
W 4:10pm-6:00pm
212D Lewisohn Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Michael Schwam-Baird
Type LECTURE
Method of Instruction In-Person
Course Description

One of the best ways to predict the future is to study the past. A dizzying amount of data is available to study elections and politics, including survey and polling data on individual preferences, beliefs, demographics, and choices; data on aggregate conditions and outcomes; and, for more recent years, a wide range of social media data. From polling analysts and pundits to campaign managers and career journalists, making sense of this data can create a competitive advantage for professionals working in the field of politics. By analyzing the results of previous elections, insights can be gleaned to enhance understanding of the factors that contributed to electoral wins and be used to build statistical models or to create machine learning models that can predict future outcomes. Students will curate various types of data and work with starter code to build their data wrangling and computational skills. Students will learn how to explore data with these techniques, understand how they work, and derive insights and knowledge based on the analysis results.

Web Site Vergil
Department Political Analytics
Enrollment 6 students (25 max) as of 4:05PM Saturday, December 21, 2024
Subject Political Analytics
Number PS5120
Section 001
Division School of Professional Studies
Section key 20241POAN5120K001