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

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journal contribution
posted on 2019-04-09, 14:50 authored by Michael J. Mortenson, Neil Doherty, Stewart Robinson
In recent years there has been a growth in specialised analytics Masters 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 paper analyses UK Masters 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.

History

School

  • Business and Economics

Department

  • Business

Published in

Journal of the Operational Research Society

Volume

71

Issue

9

Pages

1327-1346

Citation

MORTENSON, M.J., DOHERTY, N. and ROBINSON, S., 2019. Creating a typology of analytics Master's degrees in UK universities: Implications for employers and educators. Journal of the Operational Research Society, doi:10.1080/01605682.2019.1605468.

Publisher

Taylor & Francis © Operational Research Society

Version

  • AM (Accepted Manuscript)

Publisher statement

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 14 June 2019, available online: http://www.tandfonline.com/10.1080/01605682.2019.1605468.

Acceptance date

2019-04-05

Publication date

2019-06-14

Copyright date

2020

ISSN

0160-5682

eISSN

1476-9360

Language

  • en