A Fresh Look at Introductory Data Science
A Fresh Look at Introductory Data Science
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DOI：
10.1080/10691898.2020.1804497
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发表时间：
2020-09-13
影响因子：
1.7
通讯作者：
Ellison, Victoria
中科院分区：
文献类型：
--
作者：
Cetinkaya-Rundel, Mine;Ellison, Victoria
大量庞大而复杂的可用数据集的激增，向大学提出了挑战，要求大学跟上对受过统计和计算技能培训的毕业生的需求，这些技能需要有效地规划、获取、管理、分析和传达此类数据的结果。为了跟上这种需求，吸引学生及早学习数据科学并为他们提供进入该领域的坚实机会变得越来越重要。我们提供了一个数据科学本科入门课程的案例研究，该课程旨在满足这些需求。这门课程在杜克大学开设，没有任何先决条件，面向众多有抱负的统计学和数据科学专业学生以及人文、社会科学和自然科学专业的学生。我们讨论了开设这样一门课程所带来的一系列独特的挑战，并针对这些挑战，详细讨论了课程的教学设计要素、内容、结构、计算基础设施和评估方法。我们还提供了一个存储库，其中包含所有开源的教材，以及复制文章中数字的R代码。
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 withand the R code for reproducing the figures found in the article.