Dean Abbott

About Dean Abbott

Globally recognized data science leader with over 35 years of experience in predictive analytics and machine learning. As Chief Data Scientist at Appriss Retail and President of Abbott Analytics, I help organizations harness advanced analytics to solve complex challenges across customer behavior, fraud detection, risk modeling, and beyond.Author of Applied Predictive Analytics (Wiley, 2014) and co author of The IBM SPSS Modeler Cookbook (Packt, 2013), I also served on advisory boards at UC Irvine and UC San Diego’s analytics programs and regularly teach and present at leading conferences worldwide, including full-day workshops on Generative AI & Machine Learning.Previously, I co founded SmarterHQ (acquired by Wunderkind in 2020), and was the Bodily Bicentennial Professor in Analytics at UVA Darden (2023–24).

Experience

1. Founder and Chief Data Scientist

   Abbott Analytics

   Mar 1999 — Present

   Operating since March Abbott Analytics leads organizations through the process of applying and integrating leading-edge data mining and machine learning methods to marketing, research and business endeavors. Abbott Analytics has been dedicated to improving efficiency, ROI, and regulatory compliance through machine learning.Since 2014, Abbott Analytics has primarily been the vehicle for delivering machine learning, predictive analytics, and data science content through media, books, course instruction, and interviews.Clients for Abbott Analytics have included (partial list):• Department of Treasury: IRS (LMSB), Revenue Canada, Texas Comptroller of Public Accounts• Department of Defense: US Navy, US Air Force Research Laboratory, US Marine Corps, Defense Finance Accounting Service, Sandia National Labs• Non-Profits: YMCA• Private Sector: MetLife, Nokia, Intel Corporation, Kaiser Permanente, Los Angeles Times, Alaska Airlines, Monsanto• Analytics Software Companies: IBM, SPSS, SAS, StatSoft, Neteeza, Insightful Corporation, Tibco, TeradataCourse topics (partial list)• Data Science Bootcamp for practitioners (3-days, including entire CRISP-DM process model)• Model Ensembles (Bagging, Boosting, Random Forests, Stochastic Gradient Boosting, Stacking)• Randomization methods to improve model accuracy, variable selection, model interpretation, model stability assessment.• Explaining predictive models to decision-makers

Education

• University of Virginia

  M.A.M, Applied Mathematics

  1985 — 1987

• Rensselaer Polytechnic Institute

  BS, Computational Mathematics

• Natick High School

  High School Diploma, High School/Secondary Diplomas and Certificates

  1978 — 1981

Skills

• Big Data
• Chaid
• Hadoop
• Crm
• Knime
• Logistic Regression
• Neural Networks
• Analytics
• Artificial Neural Networks
• Sentiment Analysis
• Simulations
• Cart
• Machine Learning
• Clementine
• Decision Trees
• Data Visualization
• Segmentation
• Social Network Analysis
• Business Intelligence
• Linear Regression
• Classifiers
• Cluster Analysis
• Statistical Modeling
• Statistics
• Time Series Analysis
• Classification
• Spss Clementine
• Association Rules
• Predictive Modeling
• Text Mining
• Regular Expressions
• Data Modeling
• Artificial Intelligence
• Principal Component Analysis
• Optimization
• Algorithms
• Postgresql
• Text Analytics
• Statistica
• Optimizations