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

R-lang conference sessions

Nitin Kaul and Richard Baumgartner demonstrate how Merck applies descriptive, predictive, and prescriptive analytics leveraging parallel distributed libraries and the predictive modeling capabilities of Revolution R deployed on a secure Hadoop cluster to identify the various factors for product temperature excursions and predict and prevent future temperature excursions in product shipments.
Join expert Jerry Overton as he explains how to make the business and technical aspects of your data strategy work together for best results.
Garrett Grolemund and Nathan Stephens explore the new sparklyr package by RStudio, which provides a familiar interface between the R language and Apache Spark and communicates with the Spark SQL and the Spark ML APIs so R users can easily manipulate and analyze data at scale.
Xiangrui Meng explores recent community efforts to extend SparkR for scalable advanced analytics—including summary statistics, single-pass approximate algorithms, and machine-learning algorithms ported from Spark MLlib—and shows how to integrate existing R packages with SparkR to accelerate existing R workflows.