Using R for scalable data analytics: From single machines to Hadoop Spark clusters

Join in to learn how to do scalable, end-to-end data science in R on single machines as well as on Spark clusters. You'll be assigned an individual Spark cluster with all contents preloaded and software installed and use it to gain experience building, operationalizing, and consuming machine-learning models using distributed functions in R.

R is one of the most used languages in the data science, statistics, and machine-learning (ML) community. Although open source R has a rich set of packages and functions for statistics and ML, when it comes to scalable data science, many CRAN-R users are hindered by the limitations of available functions to handle big data efficiently and a lack of knowledge about the appropriate computing environments to scale R scripts from single-node to elastic and distributed cloud services, including Spark 2.0 integrations. Vanja Paunic, Robert Horton, Hang Zhang, Srini Kumar, Mengyue Zhao, John-Mark Agosta, Mario Inchiosa, and Debraj GuhaThakurta walk you through creating end-to-end data science solutions in R on Spark clusters and consuming them in production. The tutorial materials and the scripts that are used to create the Spark clusters will be published to a public GitHub repository, so you’ll be able to create Spark clusters identical to the ones you use in the tutorial by running the scripts even after the tutorial session completes.