Teaching Data Science (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Statistical Computing, Section on Statistics and Data Science Education
Success in data science and statistics is dependent on the development of both analytical and computational skills. As statistics educators we are more familiar and comfortable with teaching the former, but the latter is becoming increasingly important. The goal of this workshop is to equip educators with concrete information on content and infrastructure for painlessly introducing modern computation into a data science and/or statistics curriculum. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into choosing infrastructure and developing curriculum. Workshop attendees will work through several exercises from existing courses and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, and collaboration, version control, and automated feedback with Git/GitHub. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools.
This workshop is aimed at participants who are interested in the role of computing in either a Statistics or Data Science curriculum, including faculty designing new courses/programs and those interested in adding or improving a computational component to an existing course. A basic knowledge of R is assumed and familiarity with Git is preferred.
Instructor(s): Mine Cetinkaya-Rundel, Duke University
