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.

摘要

传统上，统计计算课程已经教授了特定编程语言或特定统计计算方法的语法。自从Nolan和Temple Lang发表具有开创性的论文以来，我们看到了对数据争用，可重现的研究和可视化的更多重视。这种转变更好地为学生准备了使用复杂数据集并为多个受众群体进行分析的职业生涯。但是，我们认为，现在经常需要统计学家来开发统计软件，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。我们将重点介绍四个主题来描述为满足这一需求而开发的研究生课程：编程实践，软件设计，重要的算法和数据结构以及基本的工具和方法。通过代码审查和修订以及一个学期的软件项目，学生可以练习软件工程的所有技能。该课程使学生能够扩展对统计问题的计算理解同时积累越来越多的工作统计学家所需要的软件开发方面的专业知识。我们将其视为统计和数据科学计算课程未来发展的模型。