Data Analyst Competencies: A Theory-Driven Investigation of Industry Requirements in the Field of Data Analytics

As organizations' reliance on data increases, the prevalence of data analytics programs in universities likewise increases. However, despite this specialized education, scholars still report a gap between the knowledge and skills students graduate with and those required by industry upon beginning work as an entry-level data analyst. We draw on theories of data analysis and curriculum frameworks to create an integrated theoretical model to drive our work. We then conduct an extensive analysis to identify relevant languages and tools in data analysis today and collect data from hiring managers seeking data analysts through a survey-based research method. We report the major knowledge, skills, and dispositions desired in the industry today for entry- level data analysts, including specific software platforms and applications. Our findings highlight several leading tools and a better understanding of how well data analysts are expected to know each tool and when those tools are used throughout the knowledge discovery via the data analytics lifecycle. This produces important contributions, particularly to academics working to keep data analytics programs competitive and up-to-date in today's rapidly changing landscape.