Learner Personas for Domain-Specific Data Science Educational Materials by Daniel Chen Visit https://rstats.ai/nyr/ to learn more. Abstract: Finding data science learning and teaching materials is not what educators and learners will find difficult these days. Rather, finding domain-specific materials that will resonate with learners is the current challenge. In the medical sciences, many of our learners only know about spreadsheets, and treat our data as a visualization, using colors, spaces, one-off tables, and side calculations. They lack the vocabulary to talk and work with data in a programmatic manner that integrates with other data scientists. This is a talk intended for data science educators and the education community. We adapted surveys from The Carpentries, ""How Learning Works"", and ""Teaching Tech Together"" to create a learner self-assessment survey to discover learner personas in the biomedical sciences by clustering survey results. These personas and findings were used to create a data science curriculum that is grounded in data literacy topics around spreadsheets and good data practices. Bio: Daniel Chen is a PhD candidate at Virginia Tech studying data science education in the biomedical sciences. Daniel specializes in research design, analysis and teaching scientific computing with an emphasis on R, Git, Python and Linux. Daniel is the author of Pandas for Everyone, an expansion in the Pearson series---the Python/Pandas complement to R for Everyone. Twitter: https://twitter.com/chendaniely Presented at the 2021 New York R Conference (September 9, 2021)