Materials for the Teaching data science workshop by Dr. Mine Çetinkaya-Rundel and Dr. Colin Rundel at WSC 2021
View the Project on GitHub mine-cetinkaya-rundel/teach-ds-wsc-2021
23 June 2021 8-11am ET
Online
Register
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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, and workflows for 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 primarily at participants teaching data science in an academic setting in semester-long courses, however much of the information and tooling we introduce is applicable for shorter teaching experiences like workshops and bootcamps as well. Basic knowledge of R is assumed and familiarity with Git is preferred.
R and RStudio, however participants will also have the option to carry out the exercises in RStudip Cloud so they don’t need to set up anything particularly for the workshop.
The RStudio Cloud workspace is at bit.ly/teach-ds-wsc-cloud.
Time | Activity |
---|---|
08:00 - 08:05 | Welcome |
08:05 - 08:30 | Curriculum design |
08:30 - 09:00 | Teaching the tidyverse |
09:00 - 09:40 | Computing infrastructure with RStudio Cloud |
09:40 - 09:45 |
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09:45 - 10:10 | Reproducible workflows |
10:10 - 10:50 | Interactivity and immediate feedback |
10:50 - 11:00 | Wrap up and Q&A |
Mine Çetinkaya-Rundel is Professor of the Practice position at the Department of Statistical Science at Duke University and Data Scientist and Professional Educator at RStudio. 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 dataset. Mine has been working on the OpenIntro project since its founding and as part of this project she co-authored four open-source introductory statistics textbooks (including this one!). She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera.
Colin Rundel has been teaching statistics and data science courses, with a focus on computing and spatial modeling, for the last 8 years. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.
This work is
licensed under a Creative Commons Attribution 4.0 International
License.