Machine Learning and Data Science for Economists


June 18-19, 2019 

2 days, 8:30 AM – 4:30 PM
Held in conjunction with the Canadian Association for Business Economics (CABE) and the Toronto Association for Business and Economics (TABE)
Deloitte Canada Offices
Toronto, Ontario, Canada

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 (all prices in U.S. Dollars)

NABE Member: $1,600

U.S./Canadian 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 May 15, 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:

Deloitte Canada Offices
8 Adelaide Street West, Suite 200
Toronto, Ontario, M5H 0A9

Nearby Hotels:

The following hotels are located in the area of the course location (Deloitte Canada Offices, 8 Adelaide Street West, Suite 200, Toronto, Ontario, M5H 0A9, Canada). Attendees are encouraged to search for hotels that meet their needs and budget. Please note: only a sampling of nearby hotels is provided below.

Holiday Inn Express Toronto Downtown
111 Lombard St, Toronto, ON M5C 2T9, Canada

Hilton Toronto
145 Richmond St W, Toronto, ON M5H 2L2, Canada

Executive Hotel Cosmopolitan Toronto
8 Colborne St, Toronto, ON M5E 1E1, Canada
Sheraton Centre Toronto Hotel
123 Queen St W, Toronto, ON M5H 2M9, Canada


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.