With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students. Design/methodology/approach Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers' responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments. Findings Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being "messy," as requiring more work for the student or analyst to pore through than other data sets and as involving computation. Originality/value Analysis of teachers' aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.