IGE: Transforming the Education and Training of Interdisciplinary Data Scientists (TETRIDS)

计算和数据分析技能是研究生教育中越来越重要的一部分，为学生未来的职业生涯做好准备。学术研究人员收集和分析数据，以促进科学知识。企业和政策制定者使用数据为决策提供信息。国家需要教育和培训更多的学生，使他们在数据科学方面具有深厚的技术技能和广泛的跨学科合作技能，与这些研究人员、企业和政策制定者合作，将数据转化为社会利益。授予科罗拉多大学博尔德分校的美国国家科学基金会研究生教育创新奖（IGE）将测试一项新计划的有效性，该计划旨在教育和培训跨学科数据科学家，他们可以在理论和实践之间移动，以解决现实世界影响的问题。该计划将跨学科合作理论中的创新课堂教学和学习活动与跨学科统计分析实验室（丽莎）中真实的项目的实际数据科学经验相结合。具体来说，研究生将学习如何采取有效的合作态度，如何与领域专家组织有效的会议，重点关注哪些领域，有效的沟通技巧，以及如何与合作者建立牢固的关系。与此同时，学生将学习如何通过与研究人员，企业和政策制定者合作将这些知识付诸实践，以应用数据科学来解决各种特定领域的决策问题。最终，该计划可能会导致谁可以被培训成为数据科学家，他们可以在哪里接受培训，以及当我们将数据转化为社会效益时可以实现什么。这个IGE项目的目标是评估丽莎计划如何有效地教育和培训来自各种背景的研究生成为协作数据科学家。具体而言，本项目将结合联合收割机对学生技术技能的评估、学生自我评价调查、丽莎行政记录、领域专家反馈调查以及独立专家对学生项目的评价，回答两个研究问题：1.丽莎学生在多大程度上是有效的跨学科数据科学合作者？(i.e.,与历史标准、其他学生群体和专家合作数据科学家相比，他们在项目上做得如何？）2.什么程度的数据科学技术准备足以让研究生成为有效的跨学科数据科学合作者？从这个项目中产生的知识可以为学院和大学的最佳实践提供信息，使成千上万的研究生接受教育和培训，成为有效的跨学科数据科学家。研究生教育创新（IGE）计划的重点是研究生教育的研究。IGE的目标是试验、测试和验证研究生教育的创新方法，并产生将这些方法推广到更广泛的社区所需的知识。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Computational and data analysis skills are an increasingly important part of graduate education to prepare students for their future careers. Academic researchers collect and analyze data to advance scientific knowledge. Businesses and policy-makers use data to inform decisions. The nation needs to educate and train more students with deep technical skills in data science and broad interdisciplinary collaboration skills to work with these researchers, businesses, and policy makers to convert data into benefits for society. This National Science Foundation Innovations in Graduate Education (IGE) award to the University of Colorado Boulder will test the effectiveness of a new program to educate and train interdisciplinary data scientists who can move between theory and practice to solve problems for real-world impact. This program combines innovative classroom instruction and learning activities in the theory of interdisciplinary collaboration with practical data science experience working on real projects in the Laboratory for Interdisciplinary Statistical Analysis (LISA). Specifically, graduate students will learn how to adopt effective attitudes of collaboration, how to structure effective meetings with domain experts, what to focus on to make deep contributions to the domain, effective communication skills, and how to cultivate strong relationships with their collaborators. At the same time, students will learn how to put this knowledge into practice by collaborating with researchers, businesses, and policy makers to apply data science to solve a wide variety of domain-specific problems for decision-making. Ultimately, this program may lead to a transformation in who can be trained to become data scientists, where they can be trained, and what can be achieved when we transform data into societal benefits. The goal of this IGE project is to evaluate how effectively the LISA program educates and trains graduate students from a variety of backgrounds to become collaborative data scientists. Specifically, this project will combine assessments of students’ technical skills, student self-evaluation surveys, LISA administrative records, domain expert feedback surveys, and independent expert evaluations of students’ projects to answer two research questions: 1. To what extent are LISA students effective interdisciplinary data science collaborators? (i.e., how well do they do on their projects compared to historical norms, other cohorts of students, and an expert collaborative data scientist?) 2. What degree of technical preparation in data science is sufficient for graduate students to become effective interdisciplinary data science collaborators? Knowledge generated from this project may inform best practices across colleges and universities for thousands of graduate students to be educated and trained to become effective interdisciplinary data scientists. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.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.