Expanding the Scope of Statistical Computing: Training Statisticians to Be Software Engineers

Expanding the Scope of Statistical Computing: Training Statisticians to Be Software Engineers
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扩大统计计算的范围:将统计学家培训为软件工程师

DOI:
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发表时间:
2019
影响因子:
1.7
通讯作者:
C. Genovese
C. Genovese
中科院分区:
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文献类型:
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作者:
A. Reinhart;C. Genovese

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传统上,统计计算课程教授特定编程语言的语法或特定的统计计算方法。自从诺兰和坦普尔·朗的开创性论文发表以来,我们看到了对数据整理、可重复研究和可视化的更加重视。这种转变使学生更好地为处理复杂数据集和为多种受众提供分析的职业做好准备。但是,我们认为,统计学家现在经常被要求开发统计软件,而不仅仅是分析,例如实现新分析方法的R包或集成到商业产品中的机器学习系统。这需要不同的技能。我们通过关注四个主题来描述我们开发的研究生课程:编程实践,软件设计,重要算法和数据结构,以及基本工具和方法。通过代码审查和修改,以及一个学期的软件项目,学生们练习了软件工程的所有技能。该课程允许学生扩展他们对应用于统计问题的计算的理解,同时建立软件开发方面的专业知识,这越来越成为统计学家的工作领域。我们认为这是统计学和数据科学中计算课程未来发展的一个模式。
Abstract Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since Nolan and Temple Lang’s seminal paper, we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with complex datasets and producing analyses for multiple audiences. But, we argue, statisticians are now often called upon to develop statistical software, not just analyses, such as R packages implementing new analysis methods or machine learning systems integrated into commercial products. This demands different skills. We describe a graduate course that we developed to meet this need by focusing on four themes: programming practices, software design, important algorithms and data structures, and essential tools and methods. Through code review and revision, and a semester-long software project, students practice all the skills of software engineering. The course allows students to expand their understanding of computing as applied to statistical problems while building expertise in the kind of software development that is increasingly the province of the working statistician. We see this as a model for the future evolution of the computing curriculum in statistics and data science.
DOI: 10.18637/jss.v076.i01
发表时间: 2017-01-01
影响因子: 5.8
作者:
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者: Riddell, Allen