ABSTRACT

The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with this demand, attracting students early on to data science as well as providing them a solid foray into the field becomes increasingly important. We present a case study of an introductory undergraduate course in data science that is designed to address these needs. Offered at Duke University, this course has no prerequisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course, and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course. We also offer a repository containing all teaching materials that are open-source, along with supplementary materials and the R code for reproducing the figures found in the article.

摘要

本质上庞大而复杂的大量可用数据集的激增，使大学难以满足对毕业生进行有效计划，获取，管理，分析和交流所需的统计和计算技能培训的需求。这些数据的发现。为了满足这一需求，吸引学生尽早学习数据科学以及为他们提供扎实的进军领域变得越来越重要。我们提供了一个旨在解决这些需求的数据科学入门课程的案例研究。该课程在杜克大学提供，没有任何先决条件，并为有抱负的统计学和数据科学专业以及人文，社会科学和自然科学专业的学生提供广泛的听众。我们讨论了提供此类课程所带来的独特挑战，并且针对这些挑战，我们对课程的教学设计元素，内容，结构，计算基础设施以及评估方法进行了详细讨论。我们还提供一个存储库，其中包含所有开放源代码的教学材料，以及用于复制本文中发现的数字的补充材料和R代码。