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https://github.com/matackett/open-stat-ds-ed

Materials for the JSM 2021 session "Developing and Maintaining Open Source Resources for Statistics and Data Science Education"
https://github.com/matackett/open-stat-ds-ed

data-science education jsm2021 statistics

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Materials for the JSM 2021 session "Developing and Maintaining Open Source Resources for Statistics and Data Science Education"

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README 

        
## JSM 2021 - Invited session



Monday, 8/9/2021



1:30 PM - 3:20 PM ET 



Click [here](https://jsm2021.pathable.co/meetings/virtual/S7Cp6WeAiSXEf8kWn) to join session.







### Teaching Data Science Authentically Using Open Source Educational Resources [[Slides]](https://ttimbers.github.io/oer-stat-ds-ed-jsm2021-timbers/talk/oer-stat-ds-ed-jsm2021-timbers.html) [[Additional resources]](01-timbers/README.md)

 

*Tiffany Anne Timbers (University of British Columbia)*



Given that data science has embraced open source in such a big way, we believe that teaching data science should include teaching with open source data science practices and tools, as well as sharing open source educational resources. This follows the old adage, practice what you preach. Open source educational resources have many additional benefits that extend beyond the field of data science, including cost savings for learners, quick iteration on materials for instructors, and raising the quality of the resources by facilitating collaboration. In this talk, I will discuss open source educational resources for data science that we have created at the University of British Columbia that were also built authentically using open source data science practices and tools (e.g., R, {bookdown}, Python, Jupyter, Git & GitHub), these include: 1) "Data Science: A First Introduction" - an open textbook aimed at undergraduates students taking their first course in data science, 2) syllabi, lecture notes, labs and lecture videos from courses for a professional Master's in Data Science program, and 3) interactive online learning modules aimed at mid-career learners.



[Tiffany Timbers](https://www.tiffanytimbers.com/) is an Assistant Professor of Teaching in the Department of Statistics and an Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia (UBC). She received a PhD in Neuroscience in 2012 from UBC, following which she held a Banting Postdoctoral Fellowship at Simon Fraser University where her research focused on cell biology & genomics. This postdoctoral research was data intensive and required the application of data science and statistical methodologies. After her research Postdoctoral Fellowship, Tiffany joined the founding team who developed the Master’s of Data Science program at UBC as a Postdoctoral Teaching and Learning Fellow. In 2018, she joined the Statistics Department at UBC in her current role of an Assistant Professor of Teaching. Currently she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. She primarily teaches courses on introductory statistics and data science, computer programming, reproducible workflows and collaborative software development.



### Creative Contributions and Activities for Student Engagement and Learning in Data Science Education [[Slides]](https://docs.google.com/presentation/d/e/2PACX-1vTWPAhtr7XZo0w2-QuyXpRcBTAS9VDJDseS-7j0LVidVvbL5hQ5lBjWrKUm7_ZIpr-bLo4sP_Im03P0/pub?start=false&loop=false&delayms=3000) [[Additional resources]](02-horst)



*Allison Horst (UC Santa Barbara)*



Learning and engagement barriers abound in statistics and data science education. Looming large among them are feelings of anxiety and non-belonging often experienced by beginning coders - feelings that at best make courses less approachable, and at worst can deter students from pursuing courses, degrees or futures in the field.



Creative contributions and activities can be a bridge for learners to access data science topics and skills. First, through evidence and anecdotes, I will discuss how bringing creative activities and resources into classrooms can enhance student engagement and learning. Then, I will consider who we typically think of as a “contributor” to educational resources in data science, and make a case for broadening that perception to include creative contributors - those working at the intersection of the arts and data science to make materials more welcoming, engaging, and impactful for more learners.



[Allison Horst](https://www.allisonhorst.com/) teaches math, data science, statistics, and presentation skills as an Assistant Teaching Professor at the Bren School of Environmental Science and Management (UC Santa Barbara). She also leads interdepartmental data science workshops for graduate students at UCSB, and is a co-founder and active participant in R-Ladies Santa Barbara and the UCSB TidyTuesday Coding Club. Allison earned her PhD in 2012 for her work investigating the toxicity and interactions of engineered nanoparticles in environmental microorganisms. She is also a landscape painter, illustrator and designer. Much of her creative work in recent years has been on creating an open library of illustrations for use in data science and statistics courses. She was RStudio's Artist-in-Residence from 2019 - 2020.



### OpenIntro: Building, Sustaining, Supporting, and Growing Open-Source Educational Resources [[Slides]](https://bit.ly/openintro-jsm2021) [[Additional resources]](03-cetinkaya-rundel)



*Mine Çetinkaya-Rundel (Duke University, RStudio)*



OpenIntro's ([openintro.org](https://openintro.org/)) mission is to make educational products that are free and transparent and that lower barriers to education. The products include textbooks (in print and online), supporting resources for instructors as well as for students. In this talk we will discuss how the OpenIntro project has shaped and grown over the years, our process for developing and publishing open-source textbooks at the high school and college level, and our computing resources such as interactive R tutorials and R packages as well as labs in various languages. We will also give an overview of our project organisation and tooling for authoring, collaboration, and maintenance, much of which is built with R, R Markdown, Git, and GitHub. Finally, we will discuss opportunities for getting involved for educators and students contributing to the development of open-source educational resources under the OpenIntro umbrella and beyond.



[Mine Çetinkaya-Rundel](https://www2.stat.duke.edu/~mc301/) is Professor of the Practice at the Department of Statistical Science at Duke University and Data Scientist and Professional Educator at RStudio. 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, works on the OpenIntro project, teaches the popular Statistics with R MOOC on Coursera, and is the creator and maintainer of Data Science in a Box. Mine is the recipient of the 2021 Robert V. Hogg Award For Excellence in Teaching Introductory Statistics and is the Chair-Elect for the Section on Statistical Computing.



### Discussion 



*Stephanie Hicks (Johns Hopkins Bloomberg School of Public Health)*



[Stephanie Hicks](https://www.stephaniehicks.com/) is an Assistant Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health. She is also a faculty member of the [Johns Hopkins Data Science Lab](https://jhudatascience.org), co-host of [The Corresponding Author podcast](https://twitter.com/CorrespondAuth) discussing data science in academia, and co-founder of [R-Ladies Baltimore](https://rladies-baltimore.github.io). She is also the lead principal investigator for the Open Case Studies project, an educational data science resource based on real-world problems. 








[This session](https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/MainSearchResults.cfm) was organized by Mine Çetinkaya-Rundel, chaired by [Maria Tackett](https://www.mariatackett.net)(Duke University) and sponsored by the Section on Statistics and Data Science Education, the Caucus for Women in Statistics, Section on Statistical Computing, and Section on Teaching of Statistics in the Health Sciences.