One of the challenges of teaching applied data science courses is managing individual students local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform developed to support a series of MOOCs in clinical data science. This platform was designed to restrict and log all access to health datasets while also being scalable, accessible, secure, privacy preserving, and easy to access. Over the 19 months the platform has been live it has supported the computation of more than 2300 students from 101 countries.

中文翻译：

---

开发和部署可扩展的计算平台以支持临床数据科学中的MOOC教育

教授应用数据科学课程的挑战之一是管理单个学生的本地计算环境。在教授大规模开放式在线课程（MOOC）时，这尤其具有挑战性，在该课程中，来自世界各地的学生可以使用各种类型的计算机系统进行访问。在临床数据科学教育中使用敏感的健康信息还有其他挑战。在这里，我们描述了为支持临床数据科学中的一系列MOOC而开发的计算平台的开发和性能。该平台旨在限制和记录对健康数据集的所有访问，同时还具有可伸缩性，可访问性，安全性，隐私保护性和易于访问性。在过去的19个月中，该平台已经投入使用，它为来自101个国家/地区的2300多名学生的计算提供了支持。