Thursday, April 12, 2018 – 9:00am to Friday, April 13, 2018 – 5:00pm
BC20, Victoria College
73 Queen’s Park Cresent E
Toronto, ON M5S 2C3
(Enter from 95 Charles Street West)
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.
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.
US $380 for students.
US $760 for employees of NISS Affiliates and CANSSI members.
US $990 for all others.
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
- Pay online with a Credit card: Please visit the NISS webpage to process your payment.
- Call or email: You can call (202) 862-4316 or write to officeadmin@NISS.org to register
- Direct questions about this course to the Instructor E. James Harner at firstname.lastname@example.org or call him on his cell phone at 304-376-4170.
- For other questions, contact email@example.com