As Independence Day inaugurates the official summer political season in the U.S. (a season that, in reality, no longer ends), we discuss in this week’s brief uplift models; our course spotlight is on
In his book Predictive Analytics, Eric Siegel tells the story of marketing efforts at Telenor, a Norwegian telecom, to reduce churn (customers leaving for another carrier). Sophisticated analytics were used to guide the campaigns, but the managers gradually discovered that some campaigns were backfiring: they were inducing customers to leave who otherwise might have stayed. What went wrong? It wasn’t the content of the campaigns, it was […]
Instructor Spotlight
Ken Strasma, co-founder and CEO, HaystaqDNA
Ken Strasma is a pioneer in the field of predictive analytics in high-stakes Presidential campaigns, serving as the National Targeting Director for President Obama’s historic 2008 campaign and for John Kerry’s 2004 presidential campaign. He produced the predictive analytics models used by the campaigns, and helped popularize the use of that technology. Strasma is now the co-founder and CEO of HaystaqDNA, a firm that provides predictive analytics and strategic consulting services for […]
Word of the Week
Model Interpretability
Model interpretability refers to the ability for a human to understand and articulate the relationship between a model’s predictors and its outcome. For linear models, including linear and logistic regression, these relationships are seen directly in the model coefficients. For black-box models like neural nets, additional procedures must be overlaid on the model to yield some understanding of these relationships.
Course Spotlight
Aug 21 – Sep 18: Persuasion Analytics and Targeting
Your instructor is Ken Strasma, National Targeting Director for President Obama’s historic 2008 campaign and for John Kerry’s 2004 presidential campaign, and the Founder and CEO of the predictive analytics consulting firm HayStaqDNA.
In this course, you will learn how to:
Find voter targets that are appropriate to a campaign phase
Design a survey instrument for use in a predictive model
Conduct an A-B test
Implement predictive models with voter data
Add uplift modeling to predict whether a voter responds better with a “treatment”
nclude test or no-test indicator as a predictor
See you in class!
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