NRT-HDR: Graduate Traineeship in Data Science Technologies and Applications

数据科学和分析是一个新兴的跨学科领域，包括计算、统计和各种应用领域，包括医学、护理、工业和商业应用等。美国目前研究生课程的一个重大缺陷是，科学家和工程师在各自的专业领域接受了良好的培训，但缺乏数据科学和分析使新的科学发现和行业应用成为可能所需的综合知识。国家科学基金会授予佛罗里达大西洋大学(FAU)的研究培训奖将通过提出一种通过体验式学习实现融合教育的新模式来解决这些缺陷。跨学科教育以和谐的方式融合不同学科，构建新知识，将学生提升到更高的认知能力和可持续的知识和技能领域。该培训项目预计将为大约160名研究生(160名)提供独特和全面的培训机会，其中包括35(35)名资助的受训人员。来自5所学院和10个系的30名教职员工将参加该项目。该项目有可能对未来数据科学专业人员的培训实践产生重大影响。课程的主要培训内容将包括开发正常化课程、为不同的应用领域创建不同的试验台、训练营、深入选修课和专业讲习班。常态化课程将用于解决进入该项目的学生的不同背景。汇聚的研究主题将集中在三个数据科学和分析领域：(I)医疗和保健应用，(Ii)行业应用，以及(Iii)数据科学和人工智能技术。为了解决这些问题，目标是为数据科学和分析专业的研究生创建一门课程，每门课程都将由来自两个不同学科的至少两名教职员工开发。为了将研究和培训融为一体，将在新成立的数据科学和人工智能实验室开发针对不同应用领域的多个试验床。每个试验台与一个研究项目有关，将包括一个计算机平台、软件工具和一套学习模块。研究项目将与作为FAU NSF产业/大学合作研究蛋糕(高级知识支持中心)成员的行业合作伙伴共同制定。该计划将培养出具有技术深度并了解数据科学技术和应用的毕业生。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革意义的新模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF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。

Data science and analytics is an emerging transdisciplinary area comprising computing, statistics, and various application domains including medicine, nursing, industry and business applications among others. A significant shortcoming of the current graduate curricula in the U.S. is that scientists and engineers are well trained in their own areas of specialty but lack the integrative knowledge needed for new scientific discoveries and industry applications made possible by data science and analytics. The National Science Foundation Research Traineeship award to Florida Atlantic University (FAU) will address these shortcomings by proposing a new model of convergent education through experiential learning. Transdisciplinary education brings integration of different disciplines in a harmonious manner to construct new knowledge and uplift the student to higher domains of cognitive abilities and sustained knowledge and skills. The traineeship anticipates providing a unique and comprehensive training opportunity for approximately one hundred sixty graduate students (160), including thirty five (35) funded trainees. Thirty faculty members from five colleges and ten departments will participate in the program. The program has the potential to have a significant impact on training practices for future data science professionals. Primary training elements of the curriculum will include the development of normalization courses, the creation of different testbeds for the various application domains, boot-camps, in-depth elective courses, and professional workshops. Normalization courses will be used to address various background of students entering the program. The convergent research themes will focus on three data science and analytics areas: (i) medical and healthcare applications, (ii) industry applications, and (iii) data science and AI technologies. To address these, the goal is to create a curriculum for graduate students in data science and analytics, where each course will be developed by at least two faculty members from two different disciplines. In order to integrate research and training, multiple testbeds for different application domains will be developed in a newly created Data Science and Artificial Intelligence Laboratory. Each testbed, which relates to a research project, will include a computer platform, software tools, and a set of learning modules. Research projects will be formulated jointly with industry partners who are members of the NSF Industry/University Cooperative Research CAKE (Center for Advanced Knowledge Enablement) at FAU. The program will produce graduates with technical depth and understanding of data science technologies and applications.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.