Presented by Dean Abbott

Chief Data Scientist, SmarterHQ

UPDATED: A PDF of the presentation is available for download

Most of what data scientists do is nothing new, and much of what’s new is really a throwback to what we used to do 20 years ago. So why data science so popular now? In this talk, Dean will describe what differentiates data science from related fields like Business Intelligence, Predictive Analytics, and Statistics, and will illustrate the use of data science from case studies in customer analytics and fraud detection.


Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ and is based in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience applying advanced algorithms to real-world problems, including customer analytics, fraud detection, risk modeling, text mining, personality assessment, and more. He was named as a Mar Tech Superstar by Adweek, one of 26 trailblazers revolutionizing marketing technology. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013) and is a highly-regarded and popular keynote speaker at Advanced Analytics and Data Science conferences and meetups, is on the Advisory Boards for the UC/Irvine Predictive Analytics and the UCSD Data Science Certificate programs. He has a B.S. in Mathematics of Computation from Rensselaer (1985) and a Master of Applied Mathematics from the University of Virginia (1987).


July 20, 2017 (Chapter meetings held on third Thursday of the month)


8:30 – 9:00 am – Sign In

9:00 – 10:15 am – Presentation

10:15 – 10:30 am – Break, Chapter Announcements

10:30 – 11:30 am – Presentation continued

Standard Insurance Tower (900 SW 5th, please note Standard Ins. has multiple downtown locations)

We meet in the Atrium Room at the top of lobby escalators


Free for members (including ALL employees of corporate members)

$15 for non-members

$5 for students with valid student ID