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We develop, advance, and promote a previously existing framework called the Qualitative-Quantitative-Qualitative workflow (Q1Q2Q3, pronounced “Q-Q-Q”) to systematically guide the content of interdisciplinary collaborations and improve the teaching of statistics and data science. The Q1Q2Q3 workflow is designed to help statisticians and data scientists develop skills and techniques for collaboration to work with domain experts across academic fields, industry sectors, and organizations. The Q1Q2Q3 workflow explicitly emphasizes the importance of the qualitative context of a project, as well as the qualitative interpretation of quantitative findings. We explain Q1Q2Q3 and provide guidance for implementing each stage of the workflow. We describe how we teach Q1Q2Q3 within a statistics and data science collaboration course and present data evaluating its effectiveness. We also describe how Q1Q2Q3 can be useful for educators teaching introductory, projects-based, and technical statistics and data science courses. We believe that the Q1Q2Q3 workflow is an easy-to-implement technique that is beneficial and necessary for statistics and data science education and practice. It can be used to weave ethics into each stage of practice so that statisticians and data scientists can successfully transform evidence into action for the benefit of society.
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Photoluminescent Re 6 Q 8 I 2 (Q = S, Se) Semiconducting Cluster Compounds
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.chemmater.1c01696
