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Investigating the demand of data analytics skills in the Scottish labour market and patterns of supply in Scottish HE sector


   The Business School

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Skills Development Scotland, amongst other institutions, have highlighted the importance of data literacy and analytical skills. The aim of this project is to understand the demand and supply for data analytical skills within Scotland. The project will draw from classical theories of labour supply and demand, and pedagogical perspectives of higher education programme design, to develop empirical modelling based on data science approaches.

The project intends to develop a dataset of supply and demand of data science skills in Scotland. To examine the demand for data science skills the project will construct a dataset based on job advertisement data. Supply data will be drawn from university programme handbooks; identifying relevant data science and data analytical programme offered by Scottish universities, and constructing a dataset of the skills developed by these programmes.

The project aims to use this data to examine in what sectors and regions is the demand greatest? What particular data analytical skills are in most demand? Based on trends revealed by the data, what data analytical skills are likely to be core in the medium to long-term? The project will then examine the data analytical skills being developed and offered by the HE sector in Scotland, more specifically, Scottish Universities. The project will then examine whether HE supply is meeting the demand in Scotland, regional patterns of supply and demand and what areas require investment or development to meet the demand.

Understanding the alignment (or mismatch) between regional skill demand and supply by the HE sector also has the potential to inform on graduate retention patterns within Scotland, and how these can be improved.

Additionally, the project can inform on how regional patterns of skill demand for data analytics has changed following key events, such as Brexit and the economic uncertainty caused by the outbreak of COVID, and whether the HE sector has reacted to these changes.

The project expects to apply text mining procedures and Natural Language Programming (NLP) techniques to both the supply information and demand data to extract the skill information. These techniques will also be used to subsequently examine the overlap/alignment between supply and demand. One NLP approach that the project could make use of is topic modelling. It could be applied to examine the demand topics (potentially capturing skills or requirements), and then to the programme description information to capture supply topics (potentially revealing graduate skills). The overlaps between the topic modelling results will then be examined to explore whether there is alignment between supply and demand. Topic modelling has been previously applied to HR areas, including classifying job role skillsets.

Academic qualifications

A first degree (at least a 2.1) ideally in relevant topic such as business management, mathematics, data science, computing or a related subject with a good fundamental knowledge of quantitative methods and techniques.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Experience of fundamental quantitative methods and techniques

• Competent in a programming language (such as R or Python)

• Knowledge of quantitative methods

• Good written and oral communication skills

• Strong motivation, with evidence of independent research skills relevant to the project

• Good time management

Desirable attributes:

An understanding of Nautral Language Programming (NLP) would be desirable.


References

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.
Boselli, R., Cesarini, M., Mercorio, F., & Mezzanzanica, M. (2018). Classifying online Job Advertisements through Machine Learning. Future Generation Computer Systems, 86, 319–328. https://doi.org/10.1016/j.future.2018.03.035
De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management, 54(5), 807–817. https://doi.org/10.1016/j.ipm.2017.05.004
Mittal, S., Gupta, S., Sagar, Shamma, A., Sahni, I., & Thakur, D. N. (2020). A Performance Comparisons of Machine Learning Classification Techniques for Job Titles Using Job Descriptions (SSRN Scholarly Paper No. ID 3589962). Rochester, NY: Social Science Research Network. https://doi.org/10.2139/ssrn.3589962

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