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

该项目的总体目标是通过建立一个多机构合作联盟，在科学和工程领域有针对性地接触数据科学。围绕数据科学的复杂挑战需要合作，因为没有一个组织可以确定和解决所有相关问题。目前对数据科学家的大量需求迫切需要进行分析真实世界数据集的培训，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。为了让本科生和研究生做好进入数据驱动的工作队伍的准备，课程和联合课程活动都是必要的。作为这一教育计划的第一步，拟议的项目旨在提出协调一致的倡议，有意开发和管理公共数据科学课程，以便在科学和工程中应用。这些课程和共同课程倡议还扩大了妇女和代表性不足的少数群体(他们在所有科技、经济和经济领域的代表性传统上都偏低)在数据科学方面的参与。任何研究成果都将传播到学术场所。拟议的活动设计了公共课程单元，包括两个部分：概念概述和背景应用。概念概述提供了标准化的材料，并有助于数据科学的教学和实践。情景应用程序建立在概念概述的基础上，为教职员工和学生带来情景感知和领域专业知识。这些公共课程单元确定了数据科学对(特定)科学和工程学科的协同效应。此外，这些单位可能成为联盟内科学和工程教师可用的数据科学教学平台的推动力。具体地说，拟议的项目寻求参与课程开发，填补学生和教师在数据科学方面的知识空白，并通过嵌入合作伙伴关系来制定可持续发展模式。此外，我们还将每年为参与机构的学生开发和主办为期一周的数据科学新兵训练营，并每年为学生和教职员工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、工具和资源可用于其他研究密集型、妇女机构和HBCU机构。整个项目将提供课程和课程，以提高至少四个机构的教职员工和学生的特定领域数据分析技能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.