Short Course in Data Science for Environmental Public Health

项目总结/摘要 几乎所有的科学领域都被大数据时代彻底改变了，数据集有可能 在环境健康方面取得重大突破。然而，数据科学的专业发展滞后 环境卫生研究人员和从业人员的背后。什么样的培训主要是贝内 特权机构。我们建议环境公共卫生数据科学短期课程， 弥合这一教育鸿沟。通过弗雷德哈钦森癌症中心数据科学实验室，我们将利用我们的 在开发教育材料、可扩展课程和可扩展研究方面拥有25年以上的历史 经验，并围绕数据科学教育建立社区，以创建这种多模式课程。的 该计划每年将为30名学习者提供支持，首先是为期两周的在线课程， 规划基础。这两周将结合最佳实践的教学讲座， 积极的动手实验活动，以实践和强化编程技能，这是一个模型， 教师在教学方面的卓越表现赢得了认可，并成功地用于培训100多名 专业学习者。参与者将一个话题一个话题地练习新技能， 易于管理。该基金会将为参与者参加为期三天的面对面强化培训做好准备。 “编码通村”，他们在那里工作的真实环境健康项目。代码通村将允许 参与者在同行代码审查，可重复性和透明度方面实践数据道德技能， 环境此外，为了确保我们能够满足不同参与者的需求，我们将 允许学习者在整个计划中和计划之外提供匿名反馈。创建 可扩展性，我们将采用配套的大规模开放式在线课程（MOOC），以便潜在的数千名 参与者可以从中贝内。我们还将通过创建年度 为希望在自己的机构或社区中推广这一课程的教员提供培训。这些运动 将由在线数据社区提供支持，参与者可以在其中进行支持、故障排除和协作 与同龄人，以及每月提醒通讯员，以帮助参与者记住他们学到的东西。我们将 利用我们现有的教师网络，从资源不足的机构招募研究人员和教师， 特别是从历史上的黑人学院和大学，西班牙裔服务机构，部落学院 和大学，以及社区学院参加现场课程。本课程将免费提供 一半的参与者将获得旅行津贴，以帮助打破参与障碍。 在整个过程中，学习者将使用相关的健康公平数据集，最终目标是了解 以及解决环境卫生公平性方面的差距。

Project Summary/Abstract Nearly all science fields have been drastically changed by the big data era, and datasets have the potential to create major breakthroughs in environmental health. However, professional development in data science lags behind for environmental health researchers and practitioners. What training does exist primarily benefits privileged institutions. We propose the Short Course in Data Science for Environmental Public Health to bridge this education gap. Through the Fred Hutchinson Cancer Center Data Science Lab, we will leverage our combined 25-plus year track record of developing educational materials, scalable courses, scalable research experiences, and building communities around data science education to create this multi-modal course. The program, which will empower 30 learners annually, begins with a two-week online course that solidifies R programming foundations. These two weeks will use a combination of didactic lectures on best practices and active hands-on lab activities to practice and engrain programming skills, a model for which the lead instructors have earned recognition for excellence in teaching and successfully used to train over 100 professional learners. Participants will practice new skills one topic at a time to make the content more manageable. This foundation will prepare participants for participating in a three-day in-person intensive “Code-a-thon” where they work on authentic environmental health projects. The Code-a-thon will allow participants to practice data ethics skills in peer code review, reproducibility, and transparency in a supportive environment. Additionally, to ensure that we are responsive to the needs of the diverse participants, we will allow learners a mechanism to provide anonymous feedback throughout and beyond the program. To create scalability, we will adapt a companion Massive Open Online Course (MOOC) so that potentially thousands of participants can benefit. We will also harness the strengths of in-person instruction by creating a yearly training for instructors hoping to reproduce this course in their own institution or community. These efforts will be bolstered by an online data community where participants can support, troubleshoot, and collaborate with peers, as well as monthly reminder newsleęers to help participants retain what they learn. We will work with our existing network of faculty from under-resourced institutions to recruit researchers and faculty specifically from Historically Black Colleges and Universities, Hispanic Serving Institutions, Tribal Colleges and Universities, and Community Colleges to participate in the live course. The course will be offered for free and half of the participants will receive travel stipends to help break down barriers to participation. Throughout, learners will work with relevant health equity datasets with the ultimate goal of understanding and addressing disparities in environmental health equity.