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REU Site: Data Science and Crime Analytics

REU Site: Data Science and Crime Analytics
REU 网站:数据科学和犯罪分析
批准号:
1950814
负责人:
Michael Turner
金额:
$31.3万
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
项目状态:
未结题
起止时间:
2020-03-01 至 2025-02-28

项目摘要

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中文摘要
翻译
该项目由社会,行为和经济(SBE)科学理事会的本科生研究经验(REU)网站计划资助。该计划整合了研究和教育,具有科学和社会效益。该计划针对来自不同背景的本科生,特别是那些来自研究机会有限和/或参与研究的人数不足的群体,如妇女和少数民族学生。犯罪分析REU是一个为期十周的暑期课程,旨在帮助学生培养数据科学和分析多学科领域的研究技能和技术能力,并为毕业后在数据饱和的经济中进行研究生学习或就业做好准备。具体而言,该计划旨在:1)积极招募多样化的本科生,积极参与创新研究项目,应用分析工具来做出有关犯罪的决定;(2)让学生在教育生涯的早期接触基础分析方法,希望鼓励他们参与数据科学和基于分析的研究领域;(3)让学生接触并参与需要在传统学科界限内跨专家交流与合作的研究机会;(4)通过让学生接触讲习班、研讨会和社会活动来丰富他们的专业发展并激励他们追求研究生院,从而增强学生的教育体验;(5)通过提供机会,通过年度本科生研究研讨会,年度专业会议和同行评议的期刊网点传播研究成果,培养共享的跨学科沟通技能。犯罪分析-REU网站将通过在校园内为选定的学生提供住房来运作,并为他们提供津贴,以换取他们在学院领导的研究项目上的工作。该计划是围绕一个以团队为基础的教学理念,其中学生将被选择并分配到跨学科的研究团队,将使用尖端的数据技术来探索与犯罪和刑事司法有关的问题。学生将接触到数据科学技能,包括使用大型数据集,数据挖掘和操作,机器学习,犯罪映射,定量建模和数据可视化。学生还将参观数据行业的领导者,如SAS,谷歌,苹果和夏洛特梅克伦堡警察局的犯罪分析实验室。这些研究项目将以准备和提交研究手稿在学术期刊上发表以及公开介绍他们的研究而告终。学生还将参加社会和文化丰富的活动,以及旨在为研究生院和/或数据分析领域的专业就业做准备的研讨会。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。
英文摘要
This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Social, Behavioral, and Economic (SBE) Sciences Directorate. This program integrates research and education and has both scientific and societal benefits. The program targets undergraduate students from diverse backgrounds, particularly those from groups where research opportunities are limited and/or participation in research is underrepresented such as women and minority students. The Crime Analytics REU is a ten-week summer program designed to help students develop research skills and technical abilities in the multidisciplinary fields of data science and analytics, and to prepare them for graduate study or employment in a data-saturated economy following graduation. Specifically, the program seeks to: 1) aggressively recruit a diverse subset of undergraduate students to actively participate in innovative research projects that apply analytic tools to make decisions about crime; (2) expose students to foundational analytic methods early in their educational careers in hopes of encouraging their involvement in data science and an analytics-based field of study; (3) expose and engage students to research opportunities that demand communication and cooperation across experts in traditional disciplinary boundaries; (4) enhance students’ educational experiences by exposing them to workshops, seminars, and social activities that enrich their professional development and inspire them to pursue graduate school; (5) foster shared interdisciplinary communication skills by offering opportunities to disseminate research findings via an annual undergraduate research symposium, annual professional conferences, and peer-reviewed journal outlets. The Crime Analytics-REU site will operate by housing selected students on campus, and provide them with a stipend in exchange for their work on faculty-led research projects. The program is modeled around a team-based pedagogical concept in which students will be selected and assigned to interdisciplinary research teams that will use cutting-edge data technologies to explore problems related to crime and criminal justice. Students will be exposed to data science skills that include working with large data sets, data mining and manipulation, machine learning, crime mapping, quantitative modeling, and data visualization. Students will also be taken to visit leaders in the data industry, such as SAS, Google, Apple, and the Charlotte-Mecklenburg Police Department’s Crime Analysis lab. The research projects will culminate with the preparation and submission of a research manuscript for publication in an academic journal, and a public presentation of their research. Students will also participate in social and cultural enrichment activities as well as workshops designed to prepare them for graduate school and or professional employment in the data analytics field.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.
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