Creating a typology of analytics Master's degrees in UK universities: Implications for employers and educators

被引:1
|
作者
Mortenson, Michael J. [1 ]
Doherty, Neil F. [2 ]
Robinson, Stewart [2 ]
机构
[1] Univ Warwick, WMG, Coventry, W Midlands, England
[2] Loughborough Univ, Sch Business & Econ, Loughborough LE11 3TU, Leics, England
关键词
Analytics; support vector machines; curricula development; machine learning;
D O I
10.1080/01605682.2019.1605468
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In recent years there has been a growth in specialised analytics Master's degrees, in the UK and beyond. However, there has been little research into the contents of such degrees. In particular, the role disciplines such as operational research play within them remains an under-explored area. Using a mixed-methods approach, this article analyses UK Master's degrees in analytics to determine a typology of provisions. Firstly, a support vector classifier is used to identify the traditional disciplines analytics degrees most closely align with. Secondly, a hybrid approach to analyse the modules included in analytics curricula is employed, as part of which a new metric (module topic weighting) is presented. The analysis identifies two main categories of degrees, the first aligning with machine learning and computing topics; the second operational research and business themes. The paper concludes by evaluating the implications this has for students, employers, educators and the operational research discipline.
引用
收藏
页码:1327 / 1346
页数:20
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