Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY
Danielle Dean

Danielle Dean
Principal Data Scientist Lead, Microsoft

Website | @danielleodean

Danielle Dean is a principal data scientist lead in AzureCAT within the Cloud AI Platform Division at Microsoft, where she leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Sessions

2:05pm–2:45pm Thursday, 09/29/2016
Data-driven business
Location: 1 E 15/1 E 16 Level: Non-technical
Danielle Dean (Microsoft), Amy O'Connor (Cloudera)
Average rating: ***..
(3.17, 6 ratings)
At Strata + Hadoop World 2012, Amy O'Connor and her daughter Danielle Dean shared how they learned and built data science skills at Nokia. This year, Amy and Danielle explore how the landscape in the world of data science has changed in the past four years and explain how to be successful deriving value from data today. Read more.
2:55pm–3:35pm Thursday, 09/29/2016
Data science & advanced analytics
Location: Hall 1C Level: Intermediate
Danielle Dean (Microsoft), Shaheen Gauher (Microsoft)
Average rating: ****.
(4.20, 5 ratings)
In the realm of predictive maintenance, the event of interest is an equipment failure. In real scenarios, this is usually a rare event. Unless the data collection has been taking place over a long period of time, the data will have very few of these events or, in the worst case, none at all. Danielle Dean and Shaheen Gauher discuss the various ways of building and evaluating models for such data. Read more.