The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio Public Package Manager
RStudio Package Manager
With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable.
January 21, 2021
With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable. Guided simulation exercises in R allow learners to explore concepts deeply, on their own time, and with others. They can also experiment with the simulations, try out edge cases, and challenge their assumptions, leading to more fruitful discussions. The comparison between coefficient estimates in regular, LASSO, and RIDGE regression, or how PCA performs when data are related are great examples of concepts where guided simulations can encourage learners to build intuitive knowledge. This talk explores how to use simulation exercises in R to help learners explore DS concepts and provides examples.
Chelsea Parlett-Pelleriti, Grant Fleming, Simon Couch, Richard Vogg, and Alan Feder Q&A
Chelsea Parlett-Pelleriti is a PhD Candidate and full-time instructional faculty teaching Data Science at Chapman University. Her research centers around how we can use statistics and machine learning to improve the way we analyze behavioral data. In her free time, you can find Chelsea on Twitter making stats memes or statsTikTok’s. She also writes about statistics, machine learning, and using R for various blogs.