April 2, 2021 8:45-11:45am ET April 9, 2021 1:30-4:30pm ET
EKU / Zoom
register

---

Overview

Success in data science and statistics is dependent on the development of both analytical and computational skills, and the demand for educators who are proficient at teaching both these skills is growing. The goal of this workshop is to equip educators with concrete information on content, workflows, and infrastructure for painlessly introducing modern computation with R and RStudio within a data science curriculum.

In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into developing a data science curriculum and choosing workflows and infrastructure that best support the curriculum and allow for scalability. Workshop attendees will work through several exercises from existing courses and get first-hand experience with using relevant tool-chains and techniques, including running a course on RStudio Cloud, and literate programming with R Markdown, automated feedback with learnr, and workflows for version control and collaboration.

Basic knowledge of R is assumed.

Learning objectives

Curriculum, workflow, and infrastructure design for teaching data science with R and RStudio.

RStudio Cloud

The RStudio Cloud workspace is at bit.ly/design-ds-eku.

Schedule

Day 1 - Friday, April 2

Time	Activity
08:45 - 09:00	Welcome
09:00 - 10:00	Curriculum design
10:45 - 11:00	Break
11:00 - 11:45	Teaching the tidyverse

Day 2 - Friday, April 9

Time	Activity
13:30 - 13:45	Welcome + recap
13:45 - 14:45	Computing infrastructure with RStudio Cloud
14:45 - 15:00	Break
15:00 - 15:45	Interactivity and immediate feedback
15:45 - 16:15	Reproducible workflows: R Markdown, Git, and GitHub
16:15 - 16:30	Wrap up and Q&A

Instructor

Mine Çetinkaya-Rundel is Senior Lecturer in the School of Mathematics at University of Edinburgh, Data Scientist and Professional Educator at RStudio, and Associate Professor of the Practice position at the Department of Statistical Science at Duke University. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest, an annual two-day competition in which teams of undergraduate students work to reveal insights into a rich and complex data set. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored three open-source introductory statistics textbooks. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera. Mine is an ASA and ISI Fellow and in 2021 she received the Hogg Award for Excellence in Teaching Introductory Statistics.

---

This work is licensed under a Creative Commons Attribution 4.0 International License.