Episode
Introduction to Microsoft R Open
Data Science is a strategic initiative for most companies today, who seek to understand the wealth of data now available to them to understand patterns, make forecasts, and build data-driven products and process. The open-source R language is the lingua franca of data science, and ranked #6 in popularity amongst all languages by the IEEE. If you haven't yet learned what R is all about, this webinar will bring you up to speed on the history of the R language, how it's used, and why it's so popular for developing advanced analytics applications.
In this 50-minute webinar, David Smith, R Community Lead at Microsoft, will introduce the R language and community, and give examples of R in action. In the webinar, David will demonstrate Microsoft R Open, Microsoft's enhanced distribution of open-source R. He will also cover the enhancements included in Microsoft R Open, including enhanced performance, features for reproducible programming in R, and the new CRAN Time Machine for reproducible data analysis with R packages.
Data Science is a strategic initiative for most companies today, who seek to understand the wealth of data now available to them to understand patterns, make forecasts, and build data-driven products and process. The open-source R language is the lingua franca of data science, and ranked #6 in popularity amongst all languages by the IEEE. If you haven't yet learned what R is all about, this webinar will bring you up to speed on the history of the R language, how it's used, and why it's so popular for developing advanced analytics applications.
In this 50-minute webinar, David Smith, R Community Lead at Microsoft, will introduce the R language and community, and give examples of R in action. In the webinar, David will demonstrate Microsoft R Open, Microsoft's enhanced distribution of open-source R. He will also cover the enhancements included in Microsoft R Open, including enhanced performance, features for reproducible programming in R, and the new CRAN Time Machine for reproducible data analysis with R packages.
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