REU Site: University of North Carolina at Greensboro - Complex Data Analysis using Statistical and Machine Learning Tools

如今，技术创新使得以低成本收集大量数据成为可能。对这些数据进行适当的处理和分析是对未来一代毕业生的重要培训。该奖项将支持北卡罗来纳大学格林斯博罗分校使用统计和机器学习工具进行复杂数据分析的本科生研究经验网站，该网站将在2023-2025年每年夏季为10名本科生提供为期10周的复杂数据分析培训和研究机会。该项目旨在招收来自不同背景和教育机构的学生。这项研究经验旨在激励学生参与者继续在数学科学研究生学习，并成为熟练的数据分析专业人员。该课程将向学生介绍一系列复杂数据的分析技术。研究项目将向他们介绍适用于不同类型数据复杂性的一系列技术。潜在的项目包括处理地理空间资源（如人口普查、选举和流行病学数据）和人类基因组测序数据。该项目的学生还将接受专业技能培训，并对他们的工作进行演示，提交论文发表，并参加会议。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Technological innovations now allow the collection of massive amounts of data at low cost. Appropriate handling and analysis of such data is vital training for the future generation of graduates. This award will support the University of North Carolina at Greensboro Complex Data Analysis using Statistical and Machine Learning Tools Research Experiences for Undergraduates site, which will provide 10 weeks of training and research opportunities in the analysis of complex data to ten undergraduates each summer, 2023-2025. The program will aim to recruit students from diverse backgrounds and educational institutions. This research experience is intended to motivate student participants to go on to graduate study in mathematical sciences and to become skilled data analytics professionals. The program will introduce students to a range of analysis techniques for complex data. The research projects will introduce them to a range of techniques appropriate to different types of data complexity. Potential projects include working with geospatial sources (such as Census, election, and epidemiological data) and human genome sequencing data. Students in the program will also receive professional skills training and give presentations on their work, submit papers for publication, and participate in conferences.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.