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Forecasting US Presidential Elections with Mixed Models

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In this project-based course, you will learn how to forecast US Presidential Elections. We will use mixed effects models in the R programming language to build a forecasting model for the 2020 election. The project will review how the US selects Presidents in the Electoral College, stylized facts about voting trends, the basics of mixed effects models, and how to use them in forecasting.

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Elevate your analytical skills with the "Forecasting US Presidential Elections with Mixed Models" course, designed for those passionate about personal development and data analysis. Led by experts at Coursera, this professional-level course delves into the intricacies of predicting US Presidential Elections using advanced mixed effects models in R programming. Over a comprehensive 120-hour duration, you will explore the US Electoral College system, examine historical voting trends, and grasp the fundamentals of mixed effects models. By the end of the course, you'll be adept at applying these models to forecast electoral outcomes, with a specific focus on the 2020 election. Ideal for data enthusiasts and professionals seeking to enhance their forecasting capabilities, this course offers a project-based approach ensuring practical, hands-on experience. Available under the Starter subscription, this engaging learning journey promises to enrich your understanding and application of predictive analytics in the context of US elections.