Data Science Ethics with R
Learn to identify and address ethical issues in data science including visualization misrepresentation, data privacy concerns, and algorithmic bias.
Learn to identify and address ethical issues in data science including visualization misrepresentation, data privacy concerns, and algorithmic bias.
This course addresses the critical ethical responsibilities of statisticians and data scientists when working with data. Through a focused exploration of key ethical challenges, students develop essential skills to recognize, analyze, and respond to common ethical issues in the field. The course begins by examining how data visualizations can mislead audiences and teaches strategies to create more honest and accurate visual representations. Students learn to identify subtle forms of data manipulation and practice creating visualizations that convey information ethically using R. The curriculum then delves into data privacy concerns, helping students understand the fundamentals of protecting sensitive information and implementing appropriate safeguards. A significant portion of the course is dedicated to algorithmic bias, where students explore how algorithms can perpetuate or amplify existing social biases. Through case studies, readings from leading experts like Cathy O'Neil and Safiya Umoja Noble, and practical exercises in R, students gain awareness of these ethical dimensions and develop frameworks for addressing them in their professional practice. The course combines theoretical knowledge with hands-on coding practice, preparing students to approach data science with greater ethical awareness.
Instructors:
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What you'll learn
Identify and avoid misrepresentation in data visualizations
Apply ethical principles when creating data visualizations in R
Understand fundamental concepts of data privacy and protection
Recognize situations where algorithmic bias may occur
Develop strategies to mitigate potential ethical issues in data science work
Critically assess the intent behind data collection efforts
Skills you'll gain
This course includes:
0.7 Hours PreRecorded video
1 assignment
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There is 1 module in this course
This course focuses on the ethical responsibilities of data scientists and statisticians when working with data. Structured as a single comprehensive module, the course explores three critical areas of data ethics. First, it examines how data visualizations can misrepresent information, teaching students to identify misleading techniques and create more honest visual representations using R. The course includes a hands-on coding session with sector and services data to demonstrate ethical visualization practices. Second, the curriculum addresses data privacy concerns, helping students understand fundamental concepts of protecting sensitive information and implementing appropriate safeguards. Finally, the course explores algorithmic bias, defining this concept and highlighting situations where algorithms may perpetuate or amplify existing social inequalities. Through readings from experts like Alberto Cairo, Cathy O'Neil, and Safiya Umoja Noble, along with practical exercises, students develop a framework for approaching data science work with greater ethical awareness.
Data Ethics
Module 1 · 6 Hours to complete
Fee Structure
Instructors
Associate Professor of the Practice at Duke University
Dr. Mine Çetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She earned her Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Çetinkaya-Rundel is dedicated to innovative statistics pedagogy, focusing on developing student-centered learning tools for introductory statistics courses. Her recent work emphasizes teaching computation at the introductory level with a strong commitment to reproducibility and addressing the gender gap in self-efficacy within STEM fields. Additionally, her research interests include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and actively contributes to the OpenIntro project, which aims to create open-licensed educational materials that reduce barriers to education. Dr. Çetinkaya-Rundel also co-edits the Citizen Statistician blog and contributes to the "Taking a Chance in the Classroom" column in Chance Magazine.
Assistant Teaching Professor at Duke University
Dr. Elijah Meyer is an Assistant Teaching Professor in the Department of Statistical Science at Duke University, where he focuses on enhancing the teaching and learning experiences in statistics and data science. He aims to inspire students to discover their passion for working with data through innovative course creation, curriculum development, and instrument development. Dr. Meyer has a keen interest in sports analytics and enjoys playing basketball, tennis, and disc golf in his spare time. He earned both his Master's and Ph.D. in Statistics with a focus on education from Montana State University. Recently, he transitioned to North Carolina State University after completing a postdoctoral position at Duke University, where he continued his work in statistics education and data science pedagogy. For more information about his teaching and research, you can visit his personal website.
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