培养数据科学学生的就业能力:一种跨学科、以行业为重点的方法

IF 1.2 Q2 EDUCATION & EDUCATIONAL RESEARCH
Sonia Ferns, A. Phatak, S. Benson, Nina Kumagai
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引用次数: 1

摘要

在当代工作场所,对能够进行跨学科合作的数据科学家的需求很高。大学需要为数据科学专业的学生提供大量的学习机会,包括跨学科背景下的合作以及与行业合作伙伴的合作。科廷大学和澳大利亚在线实验室测试(LTOAU)合作,为数据科学专业的学生提供跨学科的、以行业为重点的学习体验。在完成项目后,学生们报告说,他们对数据科学技能的应用范围有了更好的理解。这段经历为学生提供了提高自我意识的机会,并强调了团队合作、决策和领导技能的重要性。本章介绍了基于跨学科项目的工作集成学习(IPjWIL),这是一种教育方法,为数据科学专业的学生提供必要的技能,以驾驭未来的工作世界。试点项目的结果表明,跨学科的、以行业为重点的学习经验如何提高数据科学专业学生的能力,从而提高就业能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building employability capabilities in data science students: An interdisciplinary, industry‐focused approach
In the contemporary workplace, data scientists who are capable of interdisciplinary collaboration are in high demand. Universities need to provide data science students with a plethora of learning opportunities that involve collaboration in interdisciplinary contexts and engagement with industry partners. Curtin University and Lab Tests Online Australasia (LTOAU) collaborated to provide an interdisciplinary, industry‐focused learning experience for data science students. Upon completing the project, students reported improved understanding of the range of applications for data science skills. The experience delivered opportunities for greater self‐awareness and highlighted the importance of teamwork, decision‐making and leadership skills. This chapter presents Interdisciplinary Project‐based Work‐Integrated Learning (IPjWIL), an educational approach that equips data science students with the necessary skills to navigate the future world of work. The results of the pilot project described demonstrate how interdisciplinary, industry‐focused learning experiences enhance the capabilities of data science students, thereby augmenting employability.
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来源期刊
Teaching Statistics
Teaching Statistics EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
2.10
自引率
25.00%
发文量
31
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