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.

摘要–诺兰和坦普朗的统计课程中的计算（2010）提倡将统计教育转变为广泛地包括计算。从那时起，在计算和统计方面均受过训练的个人已在新兴的数据科学领域中越来越多地被雇用。作为回应，大学开发了新的课程和计划来满足对数据科学教育不断增长的需求。为了满足这一需求，我们创建了实践中的数据科学，这是一个招收大量本科生的课程。在这里，我们提出了本课程的教学目标，其中包括：（1）将数据科学概念化为创造性的问题解决方案，重点放在基于项目的学习上；（2）优先考虑实际应用，教学和使用标准化工具和最佳实践，以及（3）通过课程作业扩大教育规模，从而可以在大规模注册的课程中进行动手和课堂学习。在整个课程中，我们还强调社会背景和数据伦理，以使学生为他们的跨学科和影响力的工作做好最充分的准备。我们着重介绍创造性的问题解决方案以及用于教学自动化弹性技能的策略，同时为学生提供创建一个独特的数据科学项目的机会，以展示其技术和创新能力。