Interdisciplinary data science training faculty workshop

跨学科数据科学培训教师研讨会

• 批准号: 2219006
• 负责人: Michael Dorff
• 金额: $ 4.94万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219006&HistoricalAwards=false
• 关键词: Interdisciplinary; data; science; training; faculty

This project aims to serve the national interest by increasing the number of faculty and undergraduate students trained in data science. Business, industry, and government agencies have a tremendous demand for science, technology, engineering, and mathematics (STEM) majors, especially those who have a background in data science. This workshop will help faculty better prepare undergraduate students for industrial careers by learning more about data science, engaging with local industrial entities to assist them on data science problems, and completing their interdisciplinary data science workshop project (e.g., mentoring undergraduate students on an interdisciplinary research project in data science, co-teaching an interdisciplinary data science class, or working together in creating an interdisciplinary data science program). In this hands-on workshop at Brigham Young University, the 40 participants will be introduced to the fields of data science, statistical learning, and machine learning Participating faculty members will attend jointly with a colleague from the same institution with one faculty being in the mathematical sciences and the other being in a different STEM discipline. They will learn Python and its data science applications, and they will gain experience using the major classification algorithms such as K-means clustering, regression, and trees on actual data. The workshop will consist of both presentations by experts in data science and a group hands-on project analyzing an actual large data set by using the different classification algorithms. Participants will collaborate and network with cross-disciplinary colleagues from their institution and others from across the U.S. Finally, each team from the same institution will use what they learn to propose, plan, and implement a joint project at their institution. The program will promote and highlight the interdisciplinary nature of data science and its ability to foster collaboration spanning mathematics, statistics, technology, engineering, economics, and the natural, physical, and computational sciences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.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.

该项目旨在通过增加接受数据科学培训的教师和本科生的数量来服务于国家利益。商业、工业和政府机构对科学、技术、工程和数学（STEM）专业有着巨大的需求，尤其是那些具有数据科学背景的专业。该研讨会将帮助教师更好地为本科生的工业生涯做好准备，通过学习更多关于数据科学的知识，与当地工业实体合作，帮助他们解决数据科学问题，并完成他们的跨学科数据科学研讨会项目（例如，指导本科生进行数据科学的跨学科研究项目，共同教授跨学科数据科学课程，或共同创建跨学科数据科学计划）。 在杨百翰大学的这个实践研讨会上，40名参与者将被介绍到数据科学，统计学习和机器学习领域。 参与的教师将与来自同一机构的一名同事共同参加，其中一名教师在数学科学领域，另一名教师在不同的STEM学科。他们将学习Python及其数据科学应用程序，并将获得使用主要分类算法的经验，如K-means聚类，回归和实际数据的树。研讨会将包括数据科学专家的演讲和一个小组动手项目，通过使用不同的分类算法分析实际的大型数据集。参与者将与来自他们机构的跨学科同事和来自美国各地的其他人合作和建立网络最后，来自同一机构的每个团队将使用他们所学的知识在他们的机构提出，计划和实施联合项目。 该计划将促进和突出数据科学的跨学科性质及其促进跨越数学，统计，技术，工程，经济学以及自然，物理和计算科学的合作的能力。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。