Applied Machine Learning
This two-day course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies are used to illustrate functionality.
This course is taught by Max Kuhn, Software Engineer and Data Scientist at RStudio and co-author of Applied Predictive Modeling. Basic familiarity with R is required.
This two-day workshop is designed for the experienced Shiny developer. It assumes that you are already familiar with Shiny. By taking this workshop, you’ll improve your understanding of shiny’s foundations and learn deeper insights to reactivity. By the end of the two days, you’ll be able to push the envelope of what you and your organizations can do with Shiny.
This course is taught by Joe Cheng, shiny creator and RStudio CTO. You’ll not only learn from the creator of shiny, but you’ll have hands on time where you can ask him the questions that stump you.
Extending the Tidyverse
This is a NEW two-day hands on workshop designed for those who have embraced the tidyverse and now want to expand it to meet their own needs. The goal of this workshop is to take you from someone who uses tidyverse functions to someone who can extend the tidyverse by:
- Writing expressive code using advanced functional programming techniques
- Designs consistent APIs using analogies to existing tools
- Uses the S3 object system to make user friendly values
- Can bundle functions with documentation and tests into a package to share with others.
This course is taught by Hadley Wickham, RStudio’s Chief Scientist. Frankly, if you don’t already know more than you wanted to about him, you shouldn’t be taking this course!
What They Forgot to Teach You About R (aka Getting S*%! Done in R)
This is a NEW two-day hands on workshop designed for experienced R and RStudio users who want to extend their R skills to make their lives easier outside of data science. You’ll learn a whole bunch of useful tools for automating common tasks in R, such as working with directories full of files, scraping websites, automatically producing reports, and more.
This course is taught by Jenny Bryan, a Software Engineer and Data Scientist at RStudio and Associate Professor of Statistics (on leave) at the University of British Columbia. Jenny is widely hailed for making github a catalyst rather than an impediment to R happiness.
Big Data with R
Learn how to use R with Hive, SQL Server, Oracle and other scalable external data sources along with Big Data clusters in this two-day workshop. We will cover how to connect, retrieve schema information, upload data, and explore data outside of R. For databases, we will focus on the dplyr, DBI and odbc packages. These packages enable us to use the same dplyr verbs inside R but are translated and sent as SQL queries. For Big Data clusters, we will also learn how to use the sparklyr package to run models inside Spark and return the results to R. We will review recommendations for connection settings, security best practices and deployment options. Throughout the workshop, we will take advantage of the new data connections available with the RStudio IDE.
This course is taught by Edgar Ruiz. Edgar Ruiz is a solutions engineer at RStudio with a background in deploying enterprise reporting and business intelligence solutions. He is the author of multiple articles and blog posts sharing analytics insights and server infrastructure for data science.