Alfonso Berumen
UCLAx Data Science Instructor & Independent Consultant
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Alfonso Berumen has over 10 years of experience in consulting where he has provided data-driven economic, statistical, and analytical support to Fortune 500 companies, government agencies, and private companies operating in a wide range of industries.
At UCLA Extension, Mr. Berumen has taught a number of courses including several that are required to earn a Data Science Certificate:
-Machine Learning Using R
-Predictive Analytics (with R)
-Intro to Data Science (with R)
-Intro to Data Analytics (with Excel)
He is currently pursuing a Doctorate in Business Administration from Pepperdine University and holds a Masters in Predictive Analytics from Northwestern University, Masters in Business Administration from the University of California, Irvine and a Bachelor of Arts in Economics from Occidental College.
Prective Analytics in R and Prediction Error
This session will cover the basics of conducting Predictive Analytics in R. The presenter will cover the basis of training/test partitions, evaluation of models, and measurement of prediction error. The presenter will use a case study approach to demonstrate why measurement of error is important when conducting Predictive Analytics. The presenter will ask students/attendees to consider not just error in the classical sense of the difference between predicted and actual values but also error in the context of the problem (i.e., dollars, number of people, or other units/quantities related to what is being modeled).
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