  The data science revolution has led to an increased interest in the practice
of data analysis. While much has been written about statistical thinking, a
complementary form of thinking that appears in the practice of data analysis is
design thinking -- the problem-solving process to understand the people for
whom a product is being designed. For a given problem, there can be significant
or subtle differences in how a data analyst (or producer of a data analysis)
constructs, creates, or designs a data analysis, including differences in the
choice of methods, tooling, and workflow. These choices can affect the data
analysis products themselves and the experience of the consumer of the data
analysis. Therefore, the role of a producer can be thought of as designing the
data analysis with a set of design principles. Here, we introduce design
principles for data analysis and describe how they can be mapped to data
analyses in a quantitative, objective and informative manner. We also provide
empirical evidence of variation of principles within and between both producers
and consumers of data analyses. Our work leads to two insights: it suggests a
formal mechanism to describe data analyses based on the design principles for
data analysis, and it provides a framework to teach students how to build data
analyses using formal design principles.
