Machine Learning and Data Science for Economists


April 17-18, 2019 

2 days, 8:30 AM – 4:30 PM
Federal Reserve Bank of Dallas - Houston Branch
Houston, TX

This course aims to speak to the value of using methods from machine learning and data science for the applied business economist.  More course information

Registration Details

NABE Member: $1,600

U.S. Government Employee: $1,675

Non-Member: $1,750

To be eligible for a refund less $50 fee, registration cancellation must be received in writing by March 13, 2019.  Questions? Please contact NABE at or phone 202-463-6223.



and scan/email to or fax 202-463-6239. Registration by email/fax includes a $25 processing fee.  Save $25 by registering online! 

Course Location:

Federal Reserve Bank of Dallas - Houston Branch
1801 Allen Parkway
Houston, TX 77019

Nearby Hotels:

Best Western Plus Downtown Inn & Suites
915 W. Dallas Street
Houston, TX 77019

Doubletree by Hilton Hotel Houston Downtown
400 Dallas Street
Houston, TX 77002

Hyatt Regency Houston
1200 Louisiana Street
Houston, TX 77002

About the Instructor

Matthew Harding
is an Econometrician and Data Scientist who develops machine learning and artificial intelligence techniques to answer Big Data questions related to individual consumption and investment decisions in areas such as health, energy, and finance. He is an Associate Professor of Economics and Statistics at UC Irvine. He holds a PhD in Economics from MIT and an MPhil in Economics from Oxford University. He directs the Deep Data Lab which conducts research into cutting edge econometric methods for the analysis of “deep data”, large and information-rich data sets derived from many seemingly unrelated sources to provide novel economic insights. At the same time his research emphasizes solutions for achieving triple-win strategies.  These are solutions that not only benefit individual consumers, but are profitable for firms, and have a large positive impact on society at large. Professor Harding advised a number of companies and agencies on economics and data science problems, including Apple, US Commodity Futures Commission, World Bank, Electric Power Research Institute (EPRI), and the Department of Justice. He is also an advisor to a number of technology startups.