Topology for Data Science: An Introductory Workshop for Undergraduates

数据科学拓扑：本科生入门研讨会

• 批准号: 1955925
• 负责人: Brittany Fasy
• 金额: $ 3.05万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955925&HistoricalAwards=false
• 关键词: Topology; Data; Science; Introductory; Workshop

Topology for Data Science (T4DS) is a day-long workshop designed to introduce undergraduate students to data science and topology, to be held on March 25, 2020, in Bozeman, MT. The workshop is scheduled to coincide with the National Conference on Undergraduate Research (NCUR)---a gathering of nearly 4,000 talented undergraduate students from across the world---hosted by Montana State University, March 26-28. This award will support the development and dissemination of workshop materials and will fund travel and participation of 28 undergraduates. An additional 22 students from local communities or those already planning to attend NCUR will also have the opportunity to participate. For a day, the participants will be immersed in the fast-growing area of data science, through the lens of topology. This workshop is the first of its kind: T4DS engages students in a hands-on, collaborative experience, requiring only discrete mathematics and a desire to try something new as prerequisites.T4DS will start with an overview of how to "think with data" through data exploration and visualization, continuing with a brief journey into the field of topology and how to use topological descriptors to summarize data. In the afternoon, students will investigate how to cluster data based on those topological descriptors, and will apply what they've learned to a new data set. The day will conclude with a reception, where a panel of five faculty members representing topology and data science will discuss their experiences with including undergraduates in research and potential career opportunities in data science. The content of T4DS will cross the disciplinary lines of computer science, statistics, and mathematics. It will blend topological data analysis, data mining, and broader data science content, delivered using an active-learning pedagogy. Most of the participants will not have any experience in topology, yet they will cluster data using topological descriptors, which will give them a glimpse into the topological data cycle. Moreover, this workshop will lay the foundation for developing materials on other aspects of data science, which will allow the organizers to broaden the offering of similar tutorials throughout Montana and surrounding states, including tribal colleges and geographically remote rural areas. After the workshop, tutorials will be distributed through Github and the Carpentries Lab---a repository of high-quality, community-reviewed, discoverable lessons--- reaching a large, diverse, international community. The conference website and workshop application can be found at http://www.montana.edu/datascience/t4ds/.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.

数据科学拓扑（T4DS）是一个为期一天的研讨会，旨在将本科生介绍到2020年3月25日在MT的Bozeman举行的数据科学和拓扑。该研讨会计划与全球近4,000名来自世界各地的才华横溢的本科生聚会相吻合 - 3月26日至28日，由蒙大拿州立大学举办的近4,000名才华横溢的本科生。该奖项将支持研讨会材料的开发和传播，并将资助28名本科生的旅行和参与。来自当地社区或计划参加NCUR的22名学生也将有机会参加。一天，参与者将通过拓扑镜头浸入数据科学快速增长的领域。该研讨会是同类工作室中的第一个：T4DS让学生参与动手，协作的经验，只需要离散的数学，并且希望尝试新事物作为先决条件。T4DS将以数据勘探和可视化的“数据思考”的概述开头，继续使用拓扑和拓扑数据的拓扑结构进行简要介绍，以进行拓扑和拓扑数据，以分析拓扑结构。下午，学生将研究如何根据这些拓扑描述来群集数据，并将他们所学的知识应用于新的数据集。这一天将以招待会结束，其中一个代表拓扑和数据科学的五个教职员工组成的小组将讨论他们的经验，其中包括在研究和数据科学领域的研究和潜在职业机会中的本科生。 T4D的内容将跨越计算机科学，统计和数学的纪律线。它将使用主动学习教学法交付拓扑数据分析，数据挖掘和更广泛的数据科学内容。大多数参与者将在拓扑方面没有任何经验，但是他们将使用拓扑描述群聚集数据，这将使他们了解拓扑数据周期。此外，该研讨会将为开发数据科学其他方面的材料奠定基础，这将使组织者能够扩大整个蒙大拿州和周边州（包括部落学院和地理上偏远的农村地区）的类似教程的产品。讲习班结束后，教程将通过Github和Carpentries Lab分发，这是一个高质量，经过社区评论，可发现的课程的存储库 - - 到达一个大型，多样化的国际社会。会议网站和研讨会申请可在http://www.montana.edu/datascience/t4ds/.t4ds/.This奖中找到反映NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来评估的支持。