Workshop: Taking Data Science to America's Emerging Workforce

该讲习班将探讨通过利用土地批出机构网络及其合作推广系统，促进全国数据科学队伍发展的创新方法。数据科学在社会所有部门--商业、政府、教育和研究--的快速增长和重要性，将改变就业的未来。这种转变将创造一种不断变化的工作组合，要求各级求职者掌握新的技能，这些技能对于参与这个新的工作世界至关重要。技能差距将在那些尚未准备好获得教育和其他相关社区资源的社会阶层最为明显--特别是在美国各地的农村地区和小城镇。讲习班将审查是否可以利用、扩大和/或加强现有的土地批出机构结构，以提供数据科学教育和培训，特别是农村地区和小城镇的数据科学教育和培训，从而获得随后可能出现的高薪工作，这些工作可能是当地的，也可能是远程的。虽然数据科学是一个潜在地可以提供许多新就业机会的领域，但它要求劳动力在适当的领域接受过良好的培训，以最好地利用这些机会。将数据科学带到美国新兴劳动力市场的研讨会将汇集来自广泛背景和广泛组织的个人--包括学术界、工业界、政府和土地出让系统。它将应对挑战，探索机会，制定建议，并提出具体的下一步行动，以在全国范围内建立数据科学实践社区。研讨会由计算机与信息科学与工程局(CEISE)、教育与人力资源局本科教育处(EHR/DUE)和国家科学基金会数学与物理科学局数学科学处(MPS/DMS)支持。这次研讨会解决了两个紧迫的问题，(1)增加美国数据科学专业人员的队伍，(2)将新兴的数据科学领域及其所代表的职业机会带到农村地区。研讨会将讨论一些问题，包括：-扩大批地机构合作扩展系统的任务和覆盖范围，将数据科学教育、研究和应用包括在内；利用这一系统在我们社会的所有范围内分配数据科学革命的好处；解决任何学术和校园协调挑战，以及在这样做的过程中联邦、州、县一级的问题。--发展合作推广系统，以适应数据科学教育、培训、研究和应用。资助新的努力将涉及什么；需要解决哪些治理问题，未来有什么想法或模式，包括将数据科学教育努力与相关领域的校园研究联系起来；--可用于数据科学和数据分析的基础广泛的教育项目的类型。是否应该探索学士学位、两年制学位(社区学院)和合作扩展认证方法的组合？--教育进入劳动力大军的新一代数据科学家，而不是再培训被现代技术取代的工人？--建立具有广泛地理区域联系的地方利益社区；--鼓励和促进在当地社区进行咨询和合同的自谋职业方法；-利用国家在数据科学教育、数据科学研究和远程教育方面的努力，包括联邦机构、州一级计划、非政府组织和私人基金会、NSF大数据中心和其他NSF劳动力发展计划的努力；-制定国家战略和计划的下一步步骤，以扩大合作扩展系统的任务范围和覆盖范围，将数据科学及其应用纳入其中。谁需要参与进来？如何协调和利用这些努力？这一奖项反映了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。

This workshop will explore innovative approaches for contributing to the development of a data science workforce across the country by leveraging the network of Land-grant Institutions and their Cooperative Extension System. The rapid growth and importance of data science in all sectors of society--across business, government, education, and research--is poised to transform the future of jobs. This transformation will create a changing mix of jobs, requiring job seekers at every level to acquire new skills that will be essential for participating in this new world of jobs. The skills gap will be most pronounced in those segments of society that do not already have ready access to educational and other associated community resources--especially in rural areas and small towns across America. The workshop will examine whether the existing land grant institution structure can be leveraged, augmented, and/or enhanced to provide, especially those in rural areas and small towns, access to data science education and training and, consequently, access to the well-paying jobs that could follow, which may be locally-based or remotely accessible. While data science is a field that can potentially deliver many new job opportunities, it requires that the workforce to be well-trained in the appropriate areas to make best use of such opportunities. The Taking Data Science to America's Emerging Workforce workshop will bring together individuals from a broad range of backgrounds and a broad range of organizations--across academia, industry, government, and the land-grant system. It will address challenges, explore opportunities, develop recommendations, and propose concrete next steps for establishing data science communities of practice across the nation. The workshop is supported by the Computer and Information Science and Engineering Directorate (CISE); the Division of Undergraduate Education of the Education and Human Resources Directorate (EHR/DUE); and, the Division of Mathematical Science of the Mathematical and Physical Sciences Directorate (MPS/DMS) of the National Science Foundation.This workshop addresses two pressing issues, (1) increasing the ranks of data science professionals in the US, and (2) taking the newly emerged field of data science, and the career opportunities that it represents, to the rural sector. The workshop will address a number of issues including:--Broadening the mandate and reach of the Cooperative Extension System of land-grant institutions to include data science education, research, and applications; utilizing this system to distribute the benefits of the data science revolution across the full reach of our society; addressing any academic and campus coordination challenges, and issues at the federal, state, county levels in doing so. --Evolution of the Cooperative Extension System to accommodate data science education, training, research, and applications. What would be involved in funding new efforts; what governance concerns would need to be addressed and what are ideas or models for the future, including linking data science education efforts to campus research in related areas;--The types of broad-based education programs that could be deployed for data science and data analytics. Are there combinations of Bachelor's, 2-year degree (Community Colleges), and Cooperative Extension certification approaches that should be explored? --Educating a new generation of data scientists entering the workforce versus retraining workers who are being displaced by modern technologies?--Establishing local communities of interest with linkages across wide geographic areas;--Encouraging and facilitating self-employment approaches to consulting and contracting in local communities;--Leveraging national efforts in data science education, data science research, and distance education, including efforts at federal agencies, state-level programs, non-governmental organizations, and private foundations, the NSF Big Data Hubs, and other NSF workforce development programs;--Developing next steps in a national strategy and plan to broaden the mandate and reach of the Cooperative Extension system to include data science and its applications. Who needs to be involved? How should these efforts be coordinated and leveraged?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.