Elements and Principles for Characterizing Variation between Data Analyses
Elements and Principles for Characterizing Variation between Data Analyses
复制标题
表征数据分析之间差异的要素和原则
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Peng
中科院分区：
文献类型：
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作者：
S. Hicks;R. Peng
数据革命导致人们对数据分析实践的兴趣增加。对于给定的问题，数据分析师如何构建或创建数据分析可能存在显著或细微的差异，包括方法，工具和工作流的选择差异。此外，数据分析师可以在数据分析中优先考虑（或不优先考虑）某些客观特征，从而导致数据分析的质量或体验差异，例如或多或少可重现的分析或或多或少详尽的分析。然而，数据分析师目前缺乏一种正式的机制来比较和对比是什么使分析彼此不同。为了解决这个问题，我们引入了一个词汇表来描述和表征数据分析之间的变化。我们将这些词汇表表示为数据分析的元素和原则，并使用它们来描述创建数据分析的实践和教学的基本概念。这导致了两个见解：它提出了一个正式的机制，以评估数据分析的基础上客观的特点，它提供了一个框架，教学生如何建立数据分析。
The data revolution has led to an increased interest in the practice of data analysis. For a given problem, there can be significant or subtle differences in how a data analyst constructs or creates a data analysis, including differences in the choice of methods, tooling, and workflow. In addition, data analysts can prioritize (or not) certain objective characteristics in a data analysis, leading to differences in the quality or experience of the data analysis, such as an analysis that is more or less reproducible or an analysis that is more or less exhaustive. However, data analysts currently lack a formal mechanism to compare and contrast what makes analyses different from each other. To address this problem, we introduce a vocabulary to describe and characterize variation between data analyses. We denote this vocabulary as the elements and principles of data analysis, and we use them to describe the fundamental concepts for the practice and teaching of creating a data analysis. This leads to two insights: it suggests a formal mechanism to evaluate data analyses based on objective characteristics, and it provides a framework to teach students how to build data analyses.