| Summary: | This study aims to explore the students' level of readiness in taking up job opportunities in big data analytics and determine the contributing factors to students' readiness. In addition, the crucial factors that need to be resolved are identified. This job field requires some significant criteria such as, willing to work as a team, self-effort, and specialised skills such as data visualisations and data storytelling, big data analysis, and basic knowledge on tools for big data analytics. Intellipaat.com, a platform that offers various professional online training courses, has ranked position in big data analytics and data science as the highest paying jobs in 2019. However, from 2019 onwards, Malaysia has been predicted to suffer a shortfall of data analysis professionals of up to 7,000-15,000. Our educational institutions are being encouraged to create more graduates to meet this need. The question arises on whether students are prepared and willing to work in this sector once they graduate. An online survey was constructed and distributed to all UiTM students enrolled in various bachelor's degrees and master's programmes. One hundred and thirty-nine students participated in this survey. A graphical tool for data tabulation was presented using a box-and-whisker plot. Additionally, correlation analysis and multiple regression were used to determine the relationship and factors that can contribute the students' readiness for job opportunities in big data analytics. The results from the box-and-whisker plot have discovered an excellent sign of students' readiness towards job opportunities in big data analytics. Correlation analyses has shown a weak to moderate relationship among factors and multiple linear regression analyses revealed the data visualisation including storytelling skill (DVSS) and teamwork (TW) have significantly given some impacts on the students' opportunity in big data analytics career. The results of this study are expected to provide insights into students' readiness for job opportunities in big data analytics. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
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