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Three Pillars of Data Science
Start Date: 11/14/2018Start Time: 4:00 PM
End Date: 11/14/2018End Time: 5:00 PM

Event Description:

About the Speaker

Cameron Davidson-Pilon has worked in many areas of applied statistics, from the evolutionary dynamics of genes to modelling of financial prices. His contributions to the community include lifelines, an implementation of survival analysis in Python, lifetimes and Bayesian Methods for Hackers, an open source book and printed book on Bayesian analysis.

His previous education was at Laurier, the University of Waterloo and the Independent University of Moscow. He currently works as a Director of Data Science at Shopify in Ottawa, Ontario.

Abstract

In this talk, I'll describe what I see the three pillars of data science are, and specifically how data scientists often fail at one of these pillars: causal inference. I'll provide three examples of where understanding causal inference has been necessary. The first example is a classic causal inference result on a dataset involving infant mortality and birth weight. The example more generally applies as a cautionary lesson to anyone doing analytics. The second example is a showcase of causal inference being used at Shopify to answer million-dollar questions about shopping behaviour. Finally, the third example is an application to fairness and unbiasedness in production machine learning systems.

Learn more about this event on the MS2Discovery website. For more information please visit our events page

Refreshments will be provided.

Location Information:
Waterloo - Lazaridis Hall
Room: LH2066
Contact Information:
Name: Professor Manuele Santoprete
Phone: 519.884.0710 x2407
Email: msantoprete@wlu.ca
Audience(s):
  • Everyone (open to public)
  • Event Organizer(s):
  • Faculty of Science
  • Cost:
    No Cost.
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