Elements and Principles of Data Analysis
Year: 2019
Type: preprint
Abstract: The revolution has led to an increased interest in the practice of analysis. As a result, there has been a proliferation of data training programs. Because science has been previously defined as an intersection of already-established fields or union of emerging technologies, the following problems arise: (1) There is little agreement about what is science; (2) Data science becomes secondary to established fields in a university setting; and (3) It is difficult to have discussions on what it means to learn about science, to teach science courses and to be a scientist. To address these problems, we propose to define the field from first principles based on the activities of people who analyze with a language and taxonomy for describing a analysis in a manner spanning disciplines. Here, we describe the elements and principles of analysis. This leads to two insights: it suggests a formal mechanism to evaluate analyses based on objective characteristics, and it provides a framework to teach students how to build analyses. We argue that the elements and principles of analysis lay the foundational framework for a more general theory of science.
Source: arXiv (Cornell University)
Authors: Stephanie C. Hicks, Roger D. Peng
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2921506637