Tomorrow: Webinar on Time-to-Event Models

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We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on time to event models. If you're trying to predict when an event will occur (for example, a consumer buying a product) or trying to infer why events occur (what were the factors that led to a component failing?), time-to-event models are a useful framework. These models are closely related to survival analysis in life sciences, except that the outcome of interest isn't “time to death” but time to some other event (e.g. in marketing, “time to purchase”). Also in today's applications the data sizes are much larger (often Hadoop scale) as all kinds of demographic, operational and sensor data are brought to bear to imrove the predictions.

I've included the webinar abstract below, and you can register here to attend (and/or be notified when the slides and replay are available). There's no charge to attend.


Presented by Revolution Analytics and DataSong

Date: Thursday, October 10, 2013
Time: 9AM – 10AM Pacific Time
Presenters: John Wallace, Founder and CEO & Tess Nesbitt, Senior Consultant, Statistician PhD, DataSong

Companies are doing a better and better job of collecting data that explains why consumers behave the way they do. These diverse data sets cause us to rethink some of the workhorse algorithms for data analysis. Specifically, the traditional binary response model leaves much room for improvement in how it embraces time. Cross–sectional models allow much rich data to fall through the cracks. We’ll discuss real-world scenarios and how to better use data with time to event modeling.

This session will cover:

  •  Several business scenarios where time to event modeling makes better use of rich data
  •  Time to event models for prediction
  •  Time to event models for inference
  •  RevoScale functions used for data analysis

Revolution Analytics Webinars: Using Time to Event Models for Prediction and Inference, presented by Revolution Analytics and DataSong

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