Chapter Meetings

July 2017: Data Science, Predictive Analytics

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Presented by Dean Abbott

Chief Data Scientist, Abbott Consulting

Most of what data scientists do is nothing new, and much of what’s new is really a throwback to what we used to do 20 years ago. So why data science so popular now? In this talk, Dean will describe what differentiates data science from related fields like Business Intelligence, Predictive Analytics, and Statistics, and will illustrate the use of data science from case studies in customer analytics and fraud detection.

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Chapter Meetings

June 2017: How Automation Can Help You Implement Governed Data Discovery

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Presented by Joseph Treadwell

Lead Solution Specialist at TimeXtender North America

In today’s age of analytics, businesses are demanding self-service access to their corporate data, while struggling to make sense of raw data at the source. In response to this, recent analyst reports have revealed a new form of BI architecture known as Governed Data Discovery.
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Chapter Meetings

May 2017: Semantic Data Mining & Deep Learning in Health Datasets

Presented by Dr. Dejing Dou

Professor, University of Oregon, Computer and Information Science Department

UPDATE: A PDF of Dr. Dejing Dou’s presentation is available for download.

Ontologies have been well used in the biomedical and health domains. Semantic data mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. The use of ontologies in mining healthcare and medicine data is a natural fit. In this talk, we focus on the use of formal (Semantic Web) ontologies in two data mining and deep learning tasks in health datasets. Continue reading “May 2017: Semantic Data Mining & Deep Learning in Health Datasets” →