TRIPODS+X:EDU: Collaborative Education: Data-driven Discovery and Alliance

该项目的总体目标是通过建立多机构合作联盟，在科学和工程领域的背景下设计有针对性的数据科学暴露。围绕数据科学的挑战的复杂性需要协作，因为没有一个组织可以识别和解决所有相关问题。当前对数据科学家的巨大需求迫切需要在分析现实世界数据集方面进行培训，并对数据科学中使用的方法和计算工具背后的数学和统计学有扎实的理解。课程和课外活动都需要为本科生和研究生进入数据驱动的劳动力做好准备。作为该教育计划的第一步，拟议的项目旨在制定协调的举措，有意地开发和管理公共数据科学课程，以应用于科学和工程。这些课程和课外活动还扩大了女性和代表性不足的少数民族（传统上在所有STEM学科中代表性不足）对数据科学的参与。任何研究成果都将在学术场所发布。拟议的活动设计了公共课程单元，包括两个部分：概念概述和上下文应用。概念概述提供了标准化的材料，有助于数据科学的教学和实践。上下文应用程序建立在概念概述的基础上，为教师和学生学习者带来态势感知和领域专业知识。这些公共课程单元确定了数据科学与（特定的）科学和工程学科之间的协同作用。此外，这些单元可以推动联盟内科学和工程学院可用的数据科学教学平台。具体而言，拟议中的项目旨在参与课程开发，填补学生和教师在数据科学知识方面的空白，并通过嵌入伙伴关系制定可持续性模式。此外，我们将每年为参与机构的学生开发和举办为期一周的数据科学训练营，并每年为学生和教师举办为期半天的数据科学研讨会。通过这笔赠款开发的共享技术、工具和资源可以适用于其他研究密集型、妇女和HBCU机构。整个项目将为至少四个机构的教师和学生提供课程和会议，以提高特定领域的数据分析技能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The overall goal of this project is to devise targeted exposure to data science in the context of science and engineering fields through the establishment of a multi-institutional cooperative alliance. The complexity of challenges surrounding data science necessitates collaboration, as no one organization can identify and resolve all relevant issues. The current massive demand for data scientists urgently requires training in analyzing real-world datasets and a solid understanding of the mathematics and statistics behind the methods and computational tools used in data science. Both curricula and co-curricular activities are needed to prepare undergraduates and graduate students to enter the data-driven workforce. As an initial step in this educational program, the proposed project aims to set forth coordinated initiatives that intentionally develop and administer communal data science curricula for application in the sciences and engineering. These curricula and co-curricular initiatives also broaden the participation of women and underrepresented minorities (who have been traditionally underrepresented across all STEM disciplines) in data science. Any research findings will be disseminated to scholarly venues. The proposed activity devises communal curricula units comprising two parts: concept overview and context application. The concept overview provides standardized material and contributes to data science pedagogy and practice. The context application builds upon the concept overview to bring situational awareness and domain expertise to faculty and student learners. These communal curricula units identify synergies of data science to (specific) science and engineering disciplines. Additionally, these units could be the impetus for a data science pedagogical platform available to science and engineering faculty within the alliance. Specifically, the proposed project seeks to engage in curriculum development, fill gaps in student and faculty knowledge in data science and enact a sustainability model by embedding partnerships. We will additionally develop and host one week-long data science boot camp each year for students at the participating institutions, and one half-day data science workshop each year for students and faculty. The shared technologies, tools and resources developed through this grant can be adapted for other research-intensive, women's and HBCU institutions. The overall project will provide courses and sessions to enhance domain-specific data analytical skills to both faculty and students across at least four institutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.