Moving Beyond Language Loyalty to Achieve More
How often have you heard the phrase “X is better than Y for data science”? This is a very common misconception among data scientists and a very broad definition of data science as a whole. For data science to be impactful, it must be credible, agile, and durable. To be able to do this, we need to embrace the differences between R and. Python. Maybe you prefer R for data wrangling and Python for modeling – that’s great! Why should serious data science be stifled for the sake of language loyalty? Data science teams need to use the wealth of tools available to deliver the most impactful results. This webinar will be a discussion among data science leaders, debunking this common myth that you have to make a choice between R and Python.
Daniel Chen is a PhD student at Virginia Tech in Genetics, Bioinformatics, and Computational Biology ( GBCB ). He is a former Posit intern working on the gradethis package and Author of Pandas for Everyone, the Python/Pandas complement to R for Everyone.
In addition to his role at Lander Analytics, Jared P. Lander is the Organizer of the New York Open Statistical Programming Meetup and the New York & Washington DC R Conferences and an Adjunct Professor at Columbia Business School. With a master's from Columbia University in statistics and a bachelor's from Muhlenberg College in mathematics, he has experience in both academic research and industry. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians. His writings on statistics can be found at jaredlander.com.
Carl Howe is the Director of Education at Posit and has been a dedicated R user since 2002. Carl leads a team of professional educators and data scientists at Posit whose mission is to train the next million R users globally. Carl regularly teaches workshops on topics such as reproducible R Markdown and Posit’s Pro Products to help R beginners become productive more quickly. Carl lives with his wife Carolyn in Stow, Massachusetts at the pleasure of his two cats.
Samantha is a Virginia native with a background in social psychology and statistics. She’s passionate about making data literacy more accessible for everyone, regardless of their means or background. When she’s not using R to analyze hip hop, she’s rewriting nasty math equations in Latex, organizing R-Ladies meetups, or getting her hands dirty in her vegetable garden. She lives with her partner, Nathan, and two big, stinky dogs.