Teaching Creative and Practical Data Science at Scale
Teaching Creative and Practical Data Science at Scale
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DOI：
10.1080/10691898.2020.1860725
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发表时间：
2021-03-10
影响因子：
1.7
通讯作者：
Ellis, Shannon E.
中科院分区：
文献类型：
--
作者：
Donoghue, Thomas;Voytek, Bradley;Ellis, Shannon E.
统计课程（2010年）中的Abstract-Nolan和Temple Lang的计算，提倡将统计教育转变为广泛包括计算。从那时起，在迅速发展的数据科学领域中，接受计算和统计培训的人已经变得越来越多。作为回应，大学开发了新的课程和计划，以满足对数据科学教育的不断增长的需求。为了满足这一需求，我们在实践中创建了数据科学，这是一个大型注册本科课程。在这里，我们介绍了教授本课程的目标，包括：（1）将数据科学概念化为创造性问题解决，重点是基于项目的学习，（2）优先考虑实际应用，教学和使用标准化的工具和最佳实践，以及（3）通过课程将教育扩展，该课程能够在大型注册课程中实践和课堂学习。在整个课程中，我们还强调社会环境和数据道德，以最好地为学生做好跨学科和有影响力的工作的准备。我们重点介绍了创造性的问题解决和教授自动化 - 弹性技能的策略，同时为学生提供了创建一个独特的数据科学项目，以展示其技术和创造性的能力。
Abstract-Nolan and Temple Lang's Computing in the Statistics Curricula (2010) advocated for a shift in statistical education to broadly include computing. In the time since, individuals with training in both computing and statistics have become increasingly employable in the burgeoning data science field. In response, universities have developed new courses and programs to meet the growing demand for data science education. To address this demand, we created Data Science in Practice, a large-enrollment undergraduate course. Here, we present our goals for teaching this course, including: (1) conceptualizing data science as creative problem solving, with a focus on project-based learning, (2) prioritizing practical application, teaching and using standardized tools and best practices, and (3) scaling education through coursework that enables hands-on and classroom learning in a large-enrollment course. Throughout this course we also emphasize social context and data ethics to best prepare students for the interdisciplinary and impactful nature of their work. We highlight creative problem solving and strategies for teaching automation-resilient skills, while providing students the opportunity to create a unique data science project that demonstrates their technical and creative capacities.