Something old, something new, something borrowed, something blue: Ways to teach data science (and learn it too!)To the Tidyverse and Beyond

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How can we effectively but gently launch new students into the world of data science? In this talk, I will discuss the ways that Albert Y. Kim and I have gone about approaching this by creating an open source, fully reproducible textbook using the bookdown package at The textbook uses the paradigm of books as versions instead of editions featuring an introductory “getting started” chapter with links to many videos and interactive content available on to support new R users. I’ll also discuss how we used #chalktalk (instead of slides) to slow down our instruction to help beginners grasp tidyverse and coding concepts. I will take a glimpse into the new infer package for statistical inference, which performs statistical inference using an expressive syntax that follows tidy design principles. Lastly, I’ll demonstrate vignettes and R Markdown reports that our students created to further support the emerging tidyverse community ecosystem and I’ll provide future goals for our project.

About the speaker

Chester Ismay
Data Science Curriculum Lead

Experienced Data Scientist with a demonstrated history of working in academia, consulting, and e-learning solving real-world problems. Skilled in teaching Statistical Modeling, R, SQL, Mathematics, Computer Science, and Sociology. Strong leader with a Doctor of Philosophy (Ph.D.) in Statistics from Arizona State University. Passionate about improving diversity in data science. Focused on improving data science education through learning by doing, collaboration, and compassionate communication.