Candidates are required to have:
- An excellent undergraduate degree with Honours in a relevant business, scientific/technological or social science subject
- A Masters degree (or equivalent) will be strongly preferred
- Students may also have other relevant experience or skills which are relevant to this project
- Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English speaking country)
Data Science/ Data analytics/ Business Analysis is one of the fastest growing areas in business. For example, Forbes quotes the data analytics market as being worth $203 billion. Part of the reason for this market being so vast is because it impacts all areas of business, industry and society.
A core part of the work of Data Scientists/ Data Analysts/ Business Analysts is to analyse data and create models to support decision makers across all areas of business and industry. The nature of the models used is wide-ranging, encompassing, for example: complex mathematical models, simulations, decision analytic tools and qualitative problem structuring methods.
When these modellers/analysts are faced with the complexities of real world problems, working as individuals or in teams, they need to identify and use the method, or methods, which best suit the specific context. Indeed, it is unlikely that the use of a single method will be sufficient to address all facets of a problem. Practitioners who are modellers/analysts tend to be pragmatic in choosing the methods they need to tackle the problems that they face. Real world problems are complex and multi-dimensional and using multiple methods enables a ‘richer and more effective wayof handling the problem situation’. In addition to this, real-life problems will often be multi-phased, which can also suggest the employment of different methods. Furthermore, it has been recognised that even if a modeller/analyst does not set out with the intention to use multiple methods, the need to do so can emerge during the life of a project. Current research in the Department of Management Science, which involves in-depth interviews with experienced analysts/modellers practitioners, supports the views expressed in the literature that the use of multiple methods is normally required in order to deal with the complexities of real world problems that decision makers face in all areas of business and industry.
There is therefore clear evidence from both practice and the literature that in order to have impact, Data scientists/Data analysts/Business analysts need to be able to combine a range of methods in practice. Graduates in these fields therefore also need to have the ability to work effectively with multiple methods. However, research indicates that this is not as simple as being trained in the individual methods and there are a range of additional considerations for practitioners beyond being able to use the individual methods. Current research is focussing on highlighting specific lessons for multiple method work, however for graduates to be able to have an impact in practice, these lessons need to be transferred into teaching.
Masters training is a common route for graduates to gain both the theoretical underpinnings and initial experiential learning required to be able to commence a career as an analyst/modeller. However, Masters training programmes are normally structured around learning and gaining experience in the use of one method at a time. This foundational knowledge is essential but the approach means that students do not consider the specific issues and challenges surrounding the use of multiple methods, for example the design processes required for mixing methods. Consequently, a key skill which would enable graduates to have greater impact as analysts/modellers and thus enable them to have a ‘more effective way of handling the problem situation’ is not being developed. Whilst there are frameworks for mixing methods that can be considered from the literature these provide little detail to support actual practice, thereby making implementation a challenging business.
Innovative approaches to teaching effective mixing of methods to build competence are required. Some examples of potential areas of research to support innovative approaches to teaching are the development of facilitation/modelling scripts to provide blueprints for guiding practitioners when combining methods in a real world intervention and identification of the key characteristics for successful team working when multiple analysts/modellers are brought together to support a multi-method approach.
In addition to potential innovations in teaching arising directly from consideration of specific content, as outlined above, the research will explore the challenge of mixing methods in practice from a pedagogical perspective. The teaching of analytical methods to support decision making in organisations has had, and continues to incorporate, a strong experiential focus since the establishment of postgraduate programmes in Operational Research some 50 years ago. This is facilitated and supported by very strong links between the academic and practitioner communities. These links are supported through the Operational Research Society who have indicated their support for the proposed research. A number of pedagogies which have recently attracted increased attention at Strathclyde seem to be well matched to the task of developing the competences outlined above. These include the jigsaw approach (www.jigsaw.org) to collaborative learning, originally developed by Aronson in USA in the 1970’s and now increasingly widely used in Higher Education and storytelling as pedagogy. These, as well as other potential approaches, will be explored as part of the research.
The aim of this research is therefore two-fold:
- · to identify the key skills that need to be developed to support analysts/modellers in using multiple methods together so that they can have an effective impact in real world interventions
- · to develop innovative teaching practices that will support analyst/modelling students in developing such skills
This project is currently not funded, but funding may be available under the Student Excellence Award (SEA) scheme.
Prof Susan Howick, Professor of Management Science
Prof Val Belton, Professor of Management Science
How to apply
At this stage, we are inviting applicants to show their interest in this unfunded project, which has potential for the right candidate to be entered into the Student Excellence Award (SEA) competition.
All applications should include:
- a cover letter indicating the candidate's relevant skills/experience and how they can contribute to this research
- a CV and relevant qualification transcripts
- two references (please refer to guidance on references)
When sending the above documents please use the following file-naming convention: fullname_typeofdocument
Upload your documents here
NB Whilst this deadline is 28th February, candidates will be considered on receipt of application. The project may be allocated before the deadline (at the discretion of the supervisors), so please ensure early submission to avoid disappointment.