Data science: A view from the White House

Data science has always been a focus at eHarmony, but recently more business units have needed data-driven models. Jonathan Morra introduces Aloha, an open source project that allows the modeling group to quickly deploy type-safe accurate models to production, and explores how eHarmony creates models with Apache Spark and how it uses them.

Yaron Haviv explains how to design real-time IoT and FSI applications, leveraging Spark with advanced data frame acceleration. Yaron then presents a detailed, practical use case, diving deep into the architectural paradigm shift that makes the powerful processing of millions of events both efficient and simple to program.

Though visualization is used in data science to understand the shape of the data, it's not widely used for statistical models, which are evaluated based on numerical summaries. Amit Kapoor explores model visualization, which aids in understanding the shape of the model, the impact of parameters and input data on the model, the fit of the model, and where it can be improved.

David Talby and Claudiu Branzan lead a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, Titan, and Elasticsearch; data science components include custom UIMA annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing.

Healthcare, a $3 trillion industry, is ripe for disruption through data science. However, there are many challenges in the journey to make healthcare a truly transparent, consumer-centric, data-driven industry. Sriram Vishwanath shares some myths and facts about data science's impact on healthcare.

Tim Williamson and Emil Eifrem explain how organizations can use graph databases to operationalize insights from big data, drawing on the real-life example of Monsantos use of graph databases to conduct real-time graph analysis of the companys data to transform the business in ways that were previously impossible.