Predictive analytics and data science have become go-to approaches to advanced analytics analyses and decision making with organizations. However, many organizations have little to no experience with these techniques within their organization to build predictive models or assess capabilities of consultants or potential hires. This topic helps the persons responsible for defining your predictive analytics strategy understand the key steps and considerations for typical predictive analytics projects without resorting to buzz-words. High-level concepts and some deep-dive ideas will be explained in ways that business stakeholders and analysts can understand.
Agenda
Faculty
Dean Abbott
Abbott Analytics
- Co-founder and chief data scientist of SmarterHQ, and President of Abbott Analytics, Inc. in San Diego, California
- Internationally recognized data mining and predictive analytics expert with more than two decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology
- Wrote, Applied Predictive Analytics, Wiley, 2014; and co-wrote, IBM SPSS Modeler Cookbook, Packt Publishing, 2013
- Highly-regarded and popular speaker at Predictive Analytics and Data Mining conferences and meetups, and is on the advisory boards for the UC/Irvine Predictive Analytics Certificate as well as the UCSD Data Mining Certificate programs
- Served several times on the program committee for the KDD Conference Industrial Track
- Master of applied mathematics degree, University of Virginia; B.S. degree in mathematics of computation, Rensselaer
- Can be contacted at [email protected] or on Twitter®: @deanabb
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