Join us at rstudio::conf(2022) to sharpen your R skills. | July 25-28th in D.C.
July 25-28th in D.C.
7/25 - 7/28 in D.C.
The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Next level training for you and your team
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
Agile data science
March 4, 2018
Agile development is a well-established practice for modern software development that gained broad adoption as software became ubiquitous in the business world. As data science matures in the organization, perhaps we are at a similar crossroads. What can data science learn from the agile approach? I'll share my experience as a data scientist in an agile product development group – what agile practices have proven most valuable, how R has enabled an agile approach, and where data science may need its own set of agile principles.
VP of Data Science at The Looma Project
Elaine works at the intersection of data science and business, leading the company to take full advantage of data as part of their analytics-driven, film-based storytelling platform. This includes data strategy, data infrastructure, reporting, and delivery of predictive analytics.