Short Course in Data Science for Environmental Public Health

项目摘要/摘要 几乎所有的科学fi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，数据科学领域的专业发展滞后 落后于环境健康研究人员和从业者。目前存在的培训主要是对fits有利的 特权机构。我们建议开设环境公共卫生数据科学短期课程，以 弥合这一教育差距。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、可扩展研究方面有25年以上的综合记录 经验，并围绕数据科学教育建立社区，以创建这门多模式的课程。这个 该计划每年将增强30名学习者的能力，首先是为期两周的在线课程，Solidifies R 编程基础。这两周将使用关于最佳实践的说教讲座和 积极的动手实验活动，以练习和强化编程技能，这是一种领导 教师在教学中的卓越表现获得了认可，并成功地培训了100多名教师 专业学习者。参与者将一次一个主题地练习新技能，以使内容更丰富 可管理的。该基金会将为参加为期三天的面对面培训的参与者做好准备 他们在真正的环境健康项目上工作的代码马拉松。Code-a-thon将允许 参与者在同行代码审查、可重复性和透明度方面练习数据道德技能 环境。此外，为了确保我们对不同参与者的需求做出反应，我们将 为学员提供一种机制，在整个课程和课程之外提供匿名反馈。要创建 可扩展性，我们将采用配套的大规模在线公开课(MOOC)，这样可能会有数千 参与者可以受益于fit。我们还将利用面对面指导的优势，创建年度 对希望在自己的机构或社区复制此课程的教师进行培训。这些efforts 将得到在线数据社区的支持，参与者可以在其中提供支持、故障排除和协作 与同行，以及每月提醒新闻ę帮助参与者记住他们所学的东西。我们会工作的 与我们现有的来自资源不足的机构的教员网络一起招聘研究人员和教员 SpeciifiCally来自历史上的黑人学院和大学、西班牙裔服务机构、部落学院 并邀请高校、社区学院参加现场直播课程。课程将是免费的off版 一半的参与者将获得旅行津贴，以帮助打破参与的障碍。 自始至终，学习者将使用相关的健康公平数据集，最终目标是理解 以及解决环境健康公平方面的差距。

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.