Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems. Unlike these disciplines, however, data science education remains heavily focused on theory and methods, and practical coursework typically revolves around cleaned or simplified data sets that have little analog in professional applications. We believe that the environment in which new data scientists are trained should more accurately reflect that in which they will eventually practice, and we propose here a data science master's degree program that takes inspiration from the residency model used in medicine. Students in the suggested program would spend their time working on a practical problem with an industry, government, or nonprofit partner, supplemented with coursework in data science methods and theory. We also discuss how this program can also be implemented in shorter formats to augment existing professional master's programs in different disciplines. This approach to learning by doing is designed to fill gaps in our current approach to data science education and ensure that students develop the skills they need to practice data science in a professional context and under the many constraints imposed by that context.

中文翻译：

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以经验为中心的有效数据科学家培训方法

像医学，心理学或教育一样，数据科学从根本上说是一门应用学科，大多数在该领域获得高级学位的学生都致力于解决实际问题。但是，与这些学科不同的是，数据科学教育仍然高度关注理论和方法，而实践课程通常围绕干净或简化的数据集展开，而这些数据集在专业应用中几乎没有类似之处。我们认为，培训新数据科学家的环境应更准确地反映他们最终将在其中进行实践的环境，我们在此提出一个数据科学硕士学位课程，该课程将从医学中使用的驻留模型中汲取灵感。建议计划的学生将花时间与行业，政府或非营利合作伙伴共同解决实际问题，辅以数据科学方法和理论的课程。我们还将讨论如何以更短的格式实施该计划，以扩展现有的不同学科专业硕士课程。这种边做边学的方法旨在填补我们当前的数据科学教育方法中的空白，并确保学生在专业背景下并在该背景下施加的许多限制下发展他们练习数据科学所需的技能。