Knowledge and Skill Sets for Big Data Profession: Assessing Student's Quality using Exploratory Factor Analysis

Abstract

Recently, several higher education institutions in Malaysia announced discontinuing some courses to ensure employability post-graduation. Finding a job that fits their qualifications is a hurdle that graduates frequently face. The International Labor Organization states that when the education and training system does not deliver the skills the labour market needs, there is a mismatch between skills and jobs. This paper presents research on big data analytics knowledge and skills acquired by students throughout their studies. A sample of 185 UiTM students from various campuses participated. These students were among those who had formally taken big data courses during their studies. Data analysis was done using exploratory factor analysis (EFA) to identify the knowledge and skills obtained. Those are important to UiTM students' preparedness for the big data profession. From the exploratory factor analysis, 26 of the 40 items are included in the six constructs with factor loadings above 0.60: teamwork, student awareness and university readiness, programming language, student's effort, data storytelling and visualization, and data organization. These factors align with the finding made by [26], which identified the key competencies the employer needs for big data professions. In conclusion, higher education institutions need to focus on these skills in improving the existing program to meet better market demand and satisfy employer expectations since the score of factor loadings obtained are just satisfactory.