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Interdisciplinary Approaches to Teaching Communication and Ethics in Data Science: A Case Study
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 333–351
Scott Thatcher   K. Scott Alberts   Tetyana Beregovska  

Authors

 
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https://doi.org/10.6339/24-JDS1129
Pub. online: 29 April 2024      Type: Education In Data Science      Open accessOpen Access

Received
1 August 2023
Accepted
4 April 2024
Published
29 April 2024

Abstract

By its nature, data science uses ideas and methodologies from computer science and statistics, along with field-specific knowledge, to describe, learn and predict. Recently, storytelling has been highlighted as an important extension of more traditional data science skills such as coding and modeling. Three courses in our new Master in Data Science and Analytic Storytelling program were designed to include interdisciplinary modules, mainly taught by faculty in storytelling-related disciplines, such as Communication and Art & Design. These courses were PDAT 622: Narrative, Argument, and Persuasion in Data Science; PDAT 624: Principles of Design in Data Visualization; and PDAT 625: Big Data Ethics and Security.
Our first cohort serves as a natural case study, allowing us to reflectively analyze our materials and an informal student survey to explore the effects of interdisciplinarity in these novel courses. Results of the student survey show that students generally found value in these interdisciplinary course components, especially in course “signature assignments,” which allow students to actively engage with course content while reinforcing technical skills from previous courses. Examples of these signature assignments are presented in this paper’s supplementary materials.

Supplementary material

 Supplementary Material
The following files are included in the supplementary material: 1. Signature assignment in Narrative, Argument and Persuasion 2. Signature assignment on algorithmic bias in bail risk assessment 3. Algorithmic bias data set, modified from the original Pro Publica data set 4. Data visualization deconstruction assignment 5. SVG file containing components of Image 1 for the deconstruction assignment 6. SVG file containing components of Image 2 for the deconstruction assignment 7. Assignment using ggplot to recreate a historic data visualization by William Playfair 8. Data set of wheat and wages for use with the Playfair reconstruction 9. Data set of British monarchs for use with the Playfair reconstruction

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Copyright
2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Open access article under the CC BY license.

Keywords
algorithmic bias communication design ethics program assessment rhetoric storytelling visualization

Funding
The authors would like to acknowledge the Missouri Department of Higher Education and Workforce Development, though FY2020 and FY2023 MoExcels grants.

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