Workshop on R & Spark: Tools for Data Science Workflows

Friday and Saturday, September 21-22, 2018

To register for this course, please send an email to RFRERET@niss.org.

COURSE OUTLINE
R is a flexible, extensible statistical computing environment, but it is limited to single-core execution. Spark is a distributed computing environment which treats R as a first-class programming language. This course introduces data structures in R and their use in functional programming workflows relevant to data science. 

The course covers the initial steps in the data science process: 

  • extracting data from source systems
  • transforming data into a tidy form
  • loading data into distributed file systems, distributed data warehouses, and NoSQL databases, i.e., ETL. 

This workflow is illustrated by using the SparkR and sparklyr package frontends to Spark from R.

SparkR and sparklyr are then used as interfaces for modeling big data using regression and classification supervised learning methods. Unsupervised learning methods, such as clustering and dimension reduction, are also covered. Additional methods, such as gradient boosting and deep learning, are illustrated using the h2o and rsparkling R packages. Finally, methods for analyzing streaming data are presented. The course finishes with an in-depth example. The infrastructure and content is containerized for easy download to your laptop using Docker.

INSTRUCTOR

E. James Harner 
E. James Harner is Professor Emeritus of Statistics at West Virginia University (WVU). He was the Chair of the Department of Statistics for 17 years and the Director of the Cancer Center Bioinformatics Core for 15 years at WVU. Currently, he is the Chairman of the Interface Foundation of North America which has partnered with the American Statistical Association to organize the annual Symposium on Data Science and Statistics (SDSS) beginning in May, 2018. The areas of his technical and research expertise include: bioinformatics, high-dimensional modeling, high-performance computing, streaming and big data modeling and statistical machine learning.

FEES: 
Free for SAMSI Postdocs
US $190 for students at Duke, UNC and NCSU
US $380 for currently enrolled students
US $760 for employees of NISS Affiliates and members of SAMSI
US $990 for all others

VENUE
Directions: The address is 79 T. W. Alexander Drive in the Research Triangle Park.  It is in the Research Commons complex. Heading west on T. W. Alexander from the Durham Freeway (Route 147), it is the third lefthand entrance into the complex.  Park in the first lot you see, in front of building 4501---there is a red sign saying MEMA on the top of the building.  SAMSI is on the third floor.

PREREQUISITES FOR THIS COURSE
Differential calculus, basic matrix algebra, a statistics course covering regression, basic R. Special rates for students. 
Operating Systems: MacOS 10.11 (El Capitan) or higher or Windows 10 Professional. Students must bring their own laptops.

HOW TO REGISTER 

Use the links in the upper right hand side of this page. - First ''Select the registration option' - then click on 'Register For This Event' button.

CONTACT US

  1. Direct questions about this course to the Instructor E. James Harner at eharner@mail.wvu.edu or call him on his cell phone at 304-376-4170.
  2. For other questions, contact officeadmin@niss.org 
Registration is not available.

Event Type

Host

The Statistical and Applied Mathematical Sciences Institute (SAMSI)

Sponsor

National Institute of Statistical Sciences

Location

SAMSI Offices
79 T. W. Alexander Drive
Research Triangle Park
,
North Carolina
,
27709
United States
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