AE  >> Vol. 7 No. 4 (July 2017)

    The Managerial Outcome-Based Curriculum Design of Business Analytics Courses—The Empirical Case of Business Intelligence and Analytics Automation

  • 全文下载: PDF(2018KB) HTML   XML   PP.197-204   DOI: 10.12677/AE.2017.74030  
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李 智:东吴大学,大数据资料管理学院,台湾 台北;国际商业机器事务公司,台湾 台北

成果导向式教育资料科学商业智能需求工程Outcome-Based Education Data Science Business Intelligence Requirement Engineering


大数据相关应用方兴未艾,企业纷纷投入资源,透过商业分析与管理指引之再利用,一方面据以改善决策品质,另一方面则期待探索隐现商机。为因应此一趋势,学训机构设计相关列学程与专业科目,一方面使企业人力资源部门按部就班养成大数据人才,另一方面亦使自我加值之个人,能充分了解资料科学之种种面向与娴熟相关分析技术等;因此,课程之设计与实施方式对于学习成果扮演关键性角色。本文以“商业智慧与分析自动化”课程为例,依据学员生属性与期望,设定适当之学习成果(Learning Outcomes),并据以规划教学纲要,一方面要使学员生了解资料科学理论,另一方面要使其与实务接轨,并以“做中学”(Learning by Doing),激发学员生思考,如何定义问题框,如何搜集有效地资料,如何进行分析以探索资料价值,如何从多元观点呈现分析结果,及训练其诠释分析方法与结果之能力,为此课程之教学目标。

The firms are investing their resources on the emerging Big Data related applications, through business analytics and reusing these implications, expecting this new approach can improve the quality of decision-making and help exploring the potential business opportunities. To respond this strong trend, many universities and the training centers began designing Big Data related programs and the associated curriculums to help the Human Resource of firms to cultivate the analytics professionals as well as the individuals who wish to self-upgrade and value-up them-selves to learn the perspectives of Data Science and to improve their analytical skills. Therefore, the curriculum design and the implementation play a key role to the learners’ outcome. This ar-ticle takes the “Business Intelligence and Analytics Automation” course as an example to articulate how to set the learning outcomes based on the learners’ attributes and their expectations and to design appropriate curriculum accordingly. The curriculum design aims to let the learners understand the theories of Data Science, link these theories to the practical problems, and possess the ability of articulating the methods-in-use and the implications from the analytical findings. Through the learning-by-doing processes, inspire the learners to think how to: (1) define the problem-frames; (2) effectively collect the data; (3) commence the analyses to distil the value from the data; and (4) illustrate the analytical results from various perspectives.

李智. 高阶经理人大数据分析课程规划与实施—以商业智慧与分析自动化课程为例[J]. 教育进展, 2017, 7(4): 197-204.


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