构建复杂的探索性数据科学编程实践

Mary Beth Kery
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引用次数: 2

摘要

尽管今天有许多专业和最终用户程序员希望参与数据科学编程,但这种形式的编程提出了独特的挑战。例如,数据科学任务通常需要探索性迭代:编码和运行许多不同的方法来达到期望的结果[1]-[3]。在我的论文研究中,我将数据科学家的行为研究与系统构建研究相结合,以构建新的支持形式,以便在这种实验驱动的工作形式中跟踪迭代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Scaffolding Complex Exploratory Data Science Programming Practices
Although a wide range of professional and end-user programmers want to engage today with data science programming, this form of programming presents unique challenges. For instance, data science tasks typically require exploratory iterations: coding and running many different approaches to reach a desired result [1]–[3]. In a body of research building towards my thesis, I have interleaved behavioral studies of data scientists with systems building research towards scaffolding new forms of support for keeping track of iterations during this experiment-driven form of work.
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