Analyzing and Visualizing Event Sequence Data by Sean Taylor
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Abstract: Many business processes can be represented as event sequence data, especially from product instrumentation in web and mobile applications. However, low-level events are challenging to wrangle, model, and visualize. As a result, analysts typically aggregate data before visualization and estimation, discarding valuable information and introducing bias. In this talk I discuss how to work with event sequences directly, with a focus on exploratory analysis and hypothesis generation, and step through interactive visualizations that support these analysis goals.
Bio: Sean J. Taylor is co-founder and chief scientist at Motif Analytics. Previously he was a data scientist and head of Lyft's Rideshare Labs and spent seven years as a research scientist on Facebook's Core Data Science team. Sean's work is at the intersection of experimentation and causal inference, with a focus on applied problems and generating business value using the latest methods. He earned his PhD in Information Systems from NYU’s Stern School of Business as well as a BS in Economics from Wharton.
Twitter: / seanjtaylor
Presented at the 2024 New York R Conference (May 16, 2024)
Hosted by Lander Analytics (landeranalytic...)
12 сен 2024
