With increasing pressure to improve graduate employment outcomes, and with students themselves becoming ever-more demanding that their courses lead to future career success, it’s imperative that universities find new and innovative ways to ensure the skills they are teaching are relevant to associated future employment.
Achieving this goal is to a large extent part contingent on there being a good match between the skills being taught and the skills employers are looking for. The more a university can incorporate the skills that are being requested in the workplace into their teaching, the better they can equip their students with the work-ready skills that will enable them to gain employment.
In order to be able to achieve this, we first need to understand which skills it is that employers are looking for. Over a number of years, we have developed a methodology which enables us to collate the job descriptions from millions of online employer job postings, and analyse them to pick out the skills terms they contain. By doing this, we have been able to create a vast Skills Library of over 30,000 terms capturing the capabilities and attributes requested by businesses, and therefore the skills that are valuable in a labour market context.
This Skills Library enables us to do a number of things. Firstly, it means we are able to take course and module descriptors, and highlight the skills terms they contain which relate to the labour market. Secondly, it enables us to analyse those skills to understand where they are found in the labour market. Thirdly, it allows us to bring these supply and demand elements together to analyse how well courses and modules reflect employer demand.
Over a number of years, we have developed a methodology which enables us to collate the job descriptions from millions of online employer job postings, and analyse them to pick out the skills terms they contain
We can look at an example. When we put a course descriptor for an MSc in Business Intelligence and Analytics through our online skills extracting tool, Skill Sync, it highlights the following skills that have relevance to the labour market: Algorithms; Apache Hadoop; Application Layers; Business Intelligence; Data Mining; Decision Making; Decision Models; Decision Theories; Distributed Data Store; Information Gathering; Infrastructure; Management; Middleware; Presentations; Python (Programming Language); R (Programming Language); Research; Simul8.
This is clearly useful in terms of seeing which actual skills being taught in the course are directly relevant to outcomes. If we then feed this group of skills into our Job Posting Analytics data, we can see which job titles are most associated with these skills: data scientists; decision science analysts; data engineers; finance business partners; big data engineers.
Although these jobs are clearly relevant to the skills taught, they perhaps don’t directly link to the kinds of business intelligence-type roles we might expect an MSc Business Intelligence and Analytics role to point to.
If, however, we approach the data from the other direction – that is starting with a cluster of job titles associated with ‘business intelligence’ and ‘business intelligence analytics’ (there are 46 in all) – we can shine a light on the sorts of skills that employers are looking for when looking to fill these roles. Below is a list of the top 15 technical and common skills highlighted by our data:
This is an interesting list that could well help those running a business intelligence analytics course in terms of understanding associated labour market skills. However, we can also run these skills back through our Skill Sync tool, in order to find the most closely associated job titles with this combination of skills: business intelligence developers; business intelligence analysts; data analysts; data engineers; power business intelligence developers.
These titles are clearly a closer match to the intended vocational outcomes of the programme than the ones we saw above, validating the group of skills identified. We could then take this data to potentially glean a number of useful insights, such as looking at demand for these jobs over a certain duration; understanding where in the country they are most in demand; and even identifying the employers who are advertising for these roles.
To recap, what we have shown above is how our skills data can be used to:
- Identify the skills terms employers use to describe the roles they need to fill
- Audit your course and module descriptors to highlight labour market skills
- Use the results to find out which job titles are connected with your courses.
This is not a question of changing what is taught – though for some courses that may be the case – but rather about being aware of what employers are seeking and perhaps tweaking courses or modules to take account of this.
By taking this approach, your university can better understand how its courses are preparing students for the world of work, so that you can make any necessary adjustments that will make them even more work-ready, and so improve graduate employment outcomes.