Preparing students for careers using business analytics and data-driven dec...

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英文文献：Preparing students for careers using business analytics and data-driven decision making-为学生在职业生涯中使用商业分析和数据驱动的决策做准备
英文文献作者：Erland Hejn Nielsen,Steen Nielsen
英文文献摘要：
Data analytics and performance measurement and management (PM&M) now seem to be deeply rooted disciplines for both professional decision makers and in the business environments. Research articles and consulting companies (e.g., AACSB, 2014) stress the importance of recruiting students with a proficiency in business analytics and of preparing students with knowledge, skills, and ability in the area of business analytics (BA) and machine learning, as these skills will help businesses process data, find patterns and relations, and make decisions and predictions. However, several ideas from BA actually go back to Anthony and Harvard Business School in 1965 and to Tukey and Princeton University in 1962, respectively. The purpose of this paper is first to discuss and show the use of BA for performance management models and decisions. Second, we discuss the content of PM&M and all the uncertainty that surrounds it. Third, we show how to combine BA and PM&M in a bachelor course, and finally we discuss the assumptions and skills necessary for students in relation to completing such a course. In this sense, the nature of our paper is inspirational. Finally, the paper reports the result from a survey made among the students who have taken the course, that is, that students’ interest in data-driven performance is best activated through a combination of hands-on learning and inspirational datasets.

数据分析和性能测量和管理(PM&M)现在似乎已经成为专业决策者和商业环境中根深蒂固的学科。研究文章和咨询公司(例如,AACSB, 2014)强调招聘学生的重要性在业务分析能力和培养学生的知识,技能和能力领域的业务分析(BA)和机器学习,这些技能将帮助企业处理数据,发现模式和关系,做出决策和预测。然而，一些来自于BA的想法实际上可以追溯到1965年的安东尼和哈佛商学院，以及1962年的杜克大学和普林斯顿大学。本文的目的是首先讨论和展示BA在绩效管理模型和决策中的应用。其次，我们讨论PM&M的内容和所有围绕它的不确定性。第三，我们展示了如何在学士课程中结合学士学位和PM&M，最后我们讨论了学生完成这类课程所必需的假设和技能。从这个意义上说，我们的论文的性质是鼓舞人心的。最后，论文报告了在选修过这门课的学生中进行的一项调查的结果，也就是说，通过动手学习和鼓舞人心的数据集的结合，学生对数据驱动的表现的兴趣能够得到最好的激发。

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