Presented By O'Reilly and Cloudera
Make Data Work
December 1–3, 2015 • Singapore
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.


1:30pm–5:00pm Tuesday, 12/01/2015
Data Science and Advanced Analytics
Location: 334 Level: Intermediate
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Average rating: ****.
(4.57, 7 ratings)
In this tutorial, you will create end-to-end predictive models based on an extensive library of machine learning algorithms included in Microsoft Azure Machine Learning studio with its R and Python language extensibility. You will then deploy and consume the model and use it for making predictions over business data. Read more.
4:00pm–4:40pm Wednesday, 12/02/2015
IoT and Real-time
Location: 324 Level: Intermediate
Tags: iot
Danielle Dean (Microsoft)
Average rating: ****.
(4.50, 6 ratings)
Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. This talk introduces the landscape and challenges of predictive maintenance applications in the industry. Through a real-world example, the talk also illustrates how to formulate a predictive maintenance problem with three machine learning models. Read more.