No Data is Clean But Most is Useful
With decades in experience as a data scientist consultant and instructor, Dean utilizes his expertise to navigate the intricacies of imperfect data turning complexities into informative opportunity and raw information into actionable insights.
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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.
Typical Engagements

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.


Upcoming Conference
“Your 'Best' Model May Not Be Good: A Business-Centric Model Selection Strategy”
Interviews featuring Dean Abbott
Our Partners






Clients

Survey analysis, likelihood to renew, recommend to a friend, satisfaction

Noncompliance for
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)
Abbott Insights
Get Insights to Case Studies, Resources, and Latest Blogs
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Large, National Health Club
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What if Generative AI turns out to be a dud?
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Invoice Fraud Detection
What Clients Say

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