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.

数据科学拓扑学(T4DS)是一个为期一天的研讨会，旨在向本科生介绍数据科学和拓扑学，将于2020年3月25日在密歇根州博兹曼举行。研讨会计划与全国本科生研究会议(NCUR)同时举行-来自世界各地的近4000名有才华的本科生聚集在一起-由蒙大拿州立大学主办，于3月26日至28日举行。这项奖励将支持讲习班材料的开发和传播，并将资助28名本科生的旅费和参与。另外22名来自当地社区或计划参加NCUR的学生也将有机会参加。在一天的时间里，参与者将通过拓扑学的镜头沉浸在快速增长的数据科学领域。这是此类研讨会的第一次：T4DS让学生参与动手的协作体验，只需要离散的数学和尝试新事物的愿望作为先决条件。T4DS将从概述如何通过数据探索和可视化来“用数据思考”开始，继续进入拓扑学领域的简短旅程，以及如何使用拓扑描述符来总结数据。下午，学生们将研究如何根据这些拓扑描述符对数据进行分类，并将所学知识应用于新的数据集。这一天将以招待会结束，一个由五名代表拓扑学和数据科学的教职员工组成的小组将讨论他们在将本科生纳入研究和数据科学潜在职业机会方面的经验。T4DS的内容将跨越计算机科学、统计学和数学的学科界限。它将融合拓扑数据分析、数据挖掘和更广泛的数据科学内容，使用主动学习教学法。大多数参与者没有任何拓扑学经验，但他们将使用拓扑描述符对数据进行集群，这将使他们对拓扑数据周期一瞥。此外，这次研讨会还将为开发有关数据科学的其他方面的材料奠定基础，这将使组织者能够在蒙大拿州和周边各州，包括部落大学和地理偏远的农村地区，扩大提供类似教程的范围。研讨会结束后，教程将通过Github和木制品实验室-一个高质量的、社区审查的、可发现的课程的储存库--分发给一个庞大的、多样化的国际社区。会议网站和研讨会申请可以在http://www.montana.edu/datascience/t4ds/.This上找到，该奖项反映了国家科学基金会的法定使命，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。