使用Microsoft Excel向MBA学生介绍规定性和预测性分析

Q3 Social Sciences
Adam Diamant
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引用次数: 0

摘要

管理人员越来越多地负责监督数据驱动的项目,这些项目包含规范性和预测性模型。此外,数据分析管道的基本知识是许多现代组织的基本要求。鉴于分析学在当今商业环境中的核心重要性,对教育教学法的需求日益增长,这种教学法既能让学生有机会学习基础知识,又能让他们熟悉这些工具的应用。然而,在引入学生可以分析和从中提取见解的现实问题与对数学概念和编程语言(如Python/R)的先决知识的需求之间存在紧张关系。因此,本文描述了一门以应用为中心的课程,该课程使用Microsoft Excel和数学编程向具有非技术背景的MBA学生介绍规范性和预测性分析的工具。在学生熟练掌握管理数据、创建优化和机器学习模型的同时,他们也接触到了更广泛的商业概念。教学评估表明,这门课程帮助学生进一步发展了他们在Microsoft Excel中的实践技能,了解了数据分析对现实世界的影响,并向他们介绍了一门他们原本认为最适合更专注于技术的专业人士的学科。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introducing Prescriptive and Predictive Analytics to MBA Students with Microsoft Excel
Managers are increasingly being tasked with overseeing data-driven projects that incorporate prescriptive and predictive models. Furthermore, basic knowledge of the data analytics pipeline is a fundamental requirement in many modern organizations. Given the central importance of analytics in today’s business environment, there is a growing demand for educational pedagogies that give students the opportunity to learn the fundamentals while also familiarizing them with how such tools are applied. However, a tension exists between the introduction of real-world problems that students can analyze and extract insight from and the need for prerequisite knowledge of mathematical concepts and programming languages such as Python/R. As a consequence, this paper describes an application-focused course that uses Microsoft Excel and mathematical programming to introduce MBA students with nontechnical backgrounds to tools from both prescriptive and predictive analytics. While students’ gain proficiency in managing data and creating optimization and machine learning models, they are also exposed to broader business concepts. Teaching evaluations indicate that the course has helped students further develop their practical skills in Microsoft Excel, gain an appreciation of the real-world impact of data analytics, and has introduced them to a discipline they originally believed was best suited for more technically focused professionals.
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来源期刊
INFORMS Transactions on Education
INFORMS Transactions on Education Social Sciences-Education
CiteScore
1.70
自引率
0.00%
发文量
34
审稿时长
52 weeks
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