Daniel Chen is a PhD student at Virginia Tech in Genetics, Bioinformatics, and Computational Biology ( GBCB ). He is a former RStudio intern working on the gradethis package and Author of Pandas for Everyone, the Python/Pandas complement to R for Everyone.
Jared Lander is Chief Data Scientist at Lander Analytics, Adjunct Professor at Columbia Business School, organizer of the New York Open Statistical Programming Meetup and the R Conferences in New York, Washington DC and Dublin. He is a Series Editor for Pearson and author of the best-selling book “R for Everyone.”
Carl drives RStudio's product marketing content strategy. Based on prior roles as both an educator and professional data scientist he is currently writing a series of thought leadership articles outlining the challenges facing Data Science Leaders today and how open source software can help Data Science teams thrive in today's business environment.
Sean leads teams to create useful, enjoyable products. Before RStudio he was a data scientist and worked on alternative vehicle models at NREL, infant sleep dynamics, and originally studied mathematics.
Lander Analytics is a New York-based data science firm, whose staff specializes in statistical consulting and infrastructure, running the full gamut of RStudio product assistance from procurement, implementation, and installation to ongoing maintenance and support. The firm also provides open-source training services for R, Python, Stan, Deep Learning, SQL, and numerous other languages. We assist organizations of all sizes on a global basis in a diverse set of enterprises that include financial services, government, energy, consumer goods, pharmaceutical, educational and professional sports.
RStudio provides the premiere open source and enterprise-ready professional software for R, including RStudio Desktop, RStudio Server, RStudio Connect, RStudio Package Manager Shiny Server, and shinyapps.io. The tidyverse, shiny, ggplot, ggvis, dplyr, knitr, R Markdown, and packrat are R packages from RStudio that every data scientist will want to enhance the value, reproducibility, and appearance of their work.