DATA ANALYTICS SPEAKER SERIES
Leading Data Science Efforts Effectively
THURS, MARCH 14TH, 2019
When: 12pm - 1pm
Where: UNC Charlotte Center City Building (320 E 9th St, Charlotte, NC 28202).
Lunch: A brown bag lunch may be brought in or Einstein’s Bagels is located in the lobby of Center City.
Parking: Parking will not be covered. We recommend using the 7th street parking deck. The pay to park lots near the building are full during week days. Parking is very limited in the area and we suggest that you walk, take the lightrail or carpool if possible. Please refrain from parking in UNCC's parking lots, as you could be ticketed.
For more information on the Center City Building, visit centercity.uncc.edu.
MEET THE SPEAKER
AUSTIN HAGERTY | LinkedIn
Title: Sr Manager, Data Science at XPO Logistics
Presentation Topic: Leading Data Science Efforts Effectively
A machine learning model developed in a vacuum is bound to fail, regardless of how well it predicts the desired outcome. A successful machine learning effort requires active partnership with stakeholders and a keen understanding of the problem to be solved. Yet, these essential elements are often overlooked or undervalued in data science initiatives. Applying a project management framework can help ensure the necessary communication and partnership occurs to increase the likelihood of success.
In this session, Austin will discuss the challenges inherent in managing data science efforts and will review several project management approaches that can be leveraged to ensure successful delivery of results.
Austin Hagerty is a data science leader, speaker and author. He has over 20 years of experience working in technology and data, covering various roles and domains including analytics, data science and project delivery. He has a Master’s in Data Science from the University of Wisconsin and holds several project management certifications including PMI’s PMP, PMI-ACP (Agile) and PgMP (Program Management). Austin has taught data bootcamps at UNC Charlotte and is coauthoring a book on machine learning.