Employability competencies needed by data analytics graduates: An analysis of online job listings

With the inception of the fourth industrial revolution, the world has undergone significant changes in terms of data availability and analysis. With the vast amounts of data available and the need for businesses to stay profitable in trying times, there is an ever-increasing requirement for data-driven solutions. Finding suitable data analytics practitioners with the knowledge and skill sets to meet these demands is difficult, posing a challenge to universities to keep up with the demands. South Africa is one of the worst-performing countries regarding mathematics standards at school level, which presents a challenge for its universities to provide employable data scientists for the market. This article identifies the competencies required of data analytics practitioners by companies operating in South Africa. This will assist universities in identifying the gaps between what they offer and what is required by the industry, enabling them to implement much-needed changes to produce graduates who can become world-class data professionals. Data were gathered from online job recruiting advertisements for data analysts, business analysts, data scientists, data engineers, and statisticians. The data were analysed using a three-step pluralistic method by which a qualitative method was used to determine broad themes and identify initial skills. These were combined with skills identified in the literature to perform text mining, which led to the creation of a structured dataset that was analysed. The most sought-after skills cover artificial intelligence, statistics, mathematics, and programming (hard skills), SQL, Python, Excel, and Power BI (data analytics software skills), and interpersonal, intrapersonal, business, analytical, communication and creativity (soft skills). Universities need to incorporate these skills in their academic programmes and inform students about the significance of developing these skills. Keywords: data scientist, employability, hard skills, pluralistic method, skills gap, soft skills, South Africa, data analytics software skills, text mining