Fostering better coding practices for data scientists
Fostering better coding practices for data scientists
复制标题
为数据科学家培养更好的编码实践
DOI：
10.1162/99608f92.97c9f60f
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Horton, Nicholas J.
中科院分区：
文献类型：
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作者：
Pruim, Randall;Gîrjau, Maria-Cristiana;Horton, Nicholas J.
许多数据科学专业的学生和从业者并没有看到花时间学习和采用良好的编码实践的价值，只要代码“有效”。然而，代码标准是现代数据科学实践的重要组成部分，它们在数据敏锐性的发展中发挥着至关重要的作用。良好的编码实践会产生更可靠的代码，节省的时间比成本更多，即使对于初学者也很重要。我们认为，有原则的编码对于高质量的数据科学实践至关重要。为了在学术课程中有效地灌输这些实践，教师和课程需要尽早开始建立这些实践，经常加强它们，并在指导学生时保持更高的标准。我们描述了数据科学良好编码实践的关键方面，并以R和Python为例进行了说明，尽管类似的标准适用于其他软件环境。实用的编码指南被组织成前十名列表。
Many data science students and practitioners don’t see the value in making time to learn and adopt good coding practices as long as the code “works”. However, code standards are an important part of modern data science practice, and they play an essential role in the development of data acumen. Good coding practices lead to more reliable code and save more time than they cost, making them important even for beginners. We believe that principled coding is vital for quality data science practice. To effectively instill these practices within academic programs, instructors and programs need to begin establishing these practices early, to reinforce them often, and to hold themselves to a higher standard while guiding students. We describe key aspects of good coding practices for data science, illustrating with examples in R and in Python, though similar standards are applicable to other software environments. Practical coding guidelines are organized into a top ten list.