Data Science in Statistics Curricula: Preparing Students to "Think with Data"
Data Science in Statistics Curricula: Preparing Students to "Think with Data"
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DOI：
10.1080/00031305.2015.1077729
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发表时间：
2015-10-02
影响因子：
1.8
通讯作者：
Ward, M. D.
中科院分区：
文献类型：
--
作者：
Hardin, J.;Hoerl, R.;Ward, M. D.
越来越多的学生正在完成统计学本科学位，并作为数据分析师进入劳动力市场。在这些职位上，他们应该了解如何使用数据库和其他数据仓库，从互联网资源中抓取数据，用多种语言编程解决复杂问题，并在算法和统计上进行思考。这些数据科学主题传统上并不是统计学本科课程的主要组成部分。因此，需要改变课程设置，以解决额外的学习成果。本文的目标是激发数据科学能力的重要性，并为教师在自己的统计课程中实施数据科学提供示例和资源。我们提供了七个机构的案例研究。这些不同的数据科学教学方法展示了课程创新，以满足新的需求。这里还包括为促进本科生参与数据和数据科学的课程设计的作业示例。[2014年11月收到。2015年7月修订）。
A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.[Received November 2014. Revised July 2015.]