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Thursday, May 23, 2024
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    Can higher ed fill the graduate data skills gap?

    The divide between the skills employers require and the skills graduates have obtained is widening, or at least it appears to be. New and emerging needs centred predominantly around data science are asking questions of universities and their ability to prepare future workforces. Paul Thurman considers whether higher ed is up to the task.

    A great deal of discussion in higher education recently has been focused on the gap between what data analytics skills are needed by employers compared with what data science skills graduates have upon completion of their programmes, both at the undergraduate and graduate. This perceived gap has been accentuated by both the emergence of quick-hit certifications in data science offered by predominantly online training academies and by some non-degree programmes from universities, as well as by employer perceptions that more focused training in analytics is a prerequisite for employment.

    In fact, many employers complain that graduates arrive to work with only basic quantitative analysis skills and require on-the-job training, at the employer’s expense, to remediate such skills gaps. While this may not always be true, the fact that a ready supply of online data science academies have sprung up to meet this demand or fill these perceived gaps from employers only further puts the spotlight on this apparent deficiency in data science acumen, whether it be real or imagined.

    As such, the gap between “supply” of and “demand” for data science skills is widening in higher education. More and more employers are demanding higher levels of data analysis skills and competence based both on their own emerging needs and on the relatively unskilled labour forces graduating from institutions of higher learning. This is one reason why so many universities and colleges are offering an array of non-degrees online, certificates, and credentials courses to alumni. A secondary benefit, of course, is to manage this negative perception of their own data science pedagogy. One of the first online “upskilling” courses Columbia Business School offered to its alumni, for example, was a course in data science and analytics.

    Degree-programme directors are also responding to this demand for more data science skills by including boot-camps and other deep-dive courses and programmes to students before commencing studies, sometimes as a requirement for admission or as part of orientation. In the past these orientation programmes focused on Excel, basic accounting and finance principles, and perhaps some marketing and operations basics. As these topics are increasingly covered during secondary school, higher education institutions’ need to include them is diminishing. Instead, what universities are finding is that students still need a bit more depth in newer tools and applications before they can successfully complete an MBA program, for example. Some degree programmes now require first-year students to take courses in both data science and coding basics as a way to close this perceived skill gap with employers and to differentiate their programmes from the competition.

    The gap between “supply” of and “demand” for data science skills is widening in higher education.

    But employers are the ones really driving the demand for analytics. They are coming to universities and colleges looking to recruit graduates and now making completion or certification of such data science skills a prerequisite to obtaining a job interview. For their existing labour forces, they are asking schools to provide the aforementioned credentials and certification opportunities.

    For example, some technology-focused US companies have come to local universities requesting things such as 200 workers certified in cybersecurity. Others are asking for hundreds of workers with credentials in data science and coding for employment in six months. These are very different demands being placed on traditional academies and formal degree programmes. In fact, this raises a huge question: should universities pivot, or at least extend, to become training academies for the next generation of labour forces? Should my university, Columbia, offer not only formal degrees in business administration and computer science but also be a place employers can come to get 100-200 people training in basic coding and business analytics skills in a matter of months without requiring them to obtain formal degrees? Should the academy that confers degrees to white collar workers also, simultaneously, offer training and certification or credential opportunities to blue collar workers as well?

    This is a broader question that many universities are facing right now, and the choices are not easy to make. What does a faculty comportment look like that handles both degree and non-degree training? How do admissions work when employers drive some needs but deans and department chairs drive others? The data science skill gap is likely only the first of many that institutions of higher education and their corresponding non-degree training academies will struggle with as more and more employers eschew degreed graduates in favour of focused, skilled workers. Until such gaps are closed, perhaps via employer-school partnerships, filling these gaps will be a challenge for employers but also an opportunity for schools and training academies that can offer quick-hit, non-degree upskilling for a broader labour force over time.

    This article was from the 2023 QS Higher Ed Report: A New Normal?. Download the full edition.