Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK
Kay Brodersen

Kay Brodersen
Senior Quantitative Analyst, Google

Website

Kay H. Brodersen is a data scientist at Google, where he works on Bayesian statistical models for causal inference in large-scale randomized experiments and anomaly detection in time series data. Kay studied at Muenster (Germany), Cambridge (UK), and Oxford (UK) and holds a PhD degree from ETH Zurich.

Sessions

13:3014:00 Tuesday, 23 May 2017
Hardcore Data Science
Location: London Suite 2/3
Level: Intermediate
Kay Brodersen (Google)
Average rating: *****
(5.00, 2 ratings)
Causal relationships empower us to understand the consequences of our actions and decide what to do next. This is why identifying causal effects has been at the heart of data science. Kay Brodersen offers an introduction to CausalImpact, a new analysis library developed at Google for identifying the causal effect of an intervention on a metric over time. Read more.