Dean Abbott is President of Abbott Analytics and currently is the Bodily Bicentennial Professor in Analytics at UVA Darden School of Business. He is an internationally recognized thought leader and innovator in data science and predictive analytics with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis and is frequently included in lists of the top pioneering and influential data scientists in the world.
Mr. Abbott is the author of "Applied Predictive Analytics" (Wiley, 2014, 2nd Edition forthcoming) and coauthor of "The IBM SPSS Modeler Cookbook" (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. Mr. Abbott holds a bachelor’s degree in computational mathematics from Rensselaer Polytechnic Institute and a master’s degree in applied mathematics from the University of Virginia.
Here's How Dean Can Help
We pride ourselves on our ability to challenge core assumptions, unpick legacy behaviors, streamline complex processes, and strike a balance between great design and functional development.
Books by Dean
A professional in his field, Dan has authored/ co-authored several books to help everyone in the data analytics path. Be it a newcomer or you’re just looking to extend your wealth of knowledge, I got you covered.
Survey analysis, likelihood to renew, recommend to a friend, satisfaction
large corporate tax returns: S-Corps (F1120S), Partnerships (F1065), C-Corps
Predict candidates who will continue Phase I training through Hell Week; Optimize candidate selection for Chief Petty Officer review.
Ensemble modeling for upsell modeling, subscription modeling
Predict tax owed by non-filer, age and recovery potential of tax debt
Predict LD50 toxicity likelihood for compounds based on chemical structure.
Predict likelihood of future severe failure of engines
Identify invoices likely to be suspicious/improper; Identify government credit card misuse
“Optimal IT”; model IT support bottlenecks and optimize prioritization of tickets
Likelihood to respond to contact; customer segmentation
Cross-sell and churn modeling; data scientist hiring process/test
Medicare customer acquisition.
Predict new subscriptions, renewed subscriptions; subscription forecasting
Predict age and recovery potential of tax debt
On-time likelihood models
Metadata analysis to determine if Ragweed is resistant to Roundup (presented at Southern Weed Society conference)
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