Abstract

The explosion of data brought about by the onset of the fourth industrial revolution has opened new opportunities for businesses to improve profitability and efficiency while driving down costs. As with other countries, the data science and analytics (DSA) industry advances to develop, and various initiatives started to bolster competitiveness - self-organized events for data practitioners, government-funded capability building, and the creation of data science curricula across several Philippine universities. This paper contributes to three different but connected components: (1) demand assessment - assessed the current demand for statistics skills in the data science industry in the Philippines, (2) supply evaluation - evaluated the readiness and alignment of current statistics course offerings in various undergraduate courses in the Philippines, and (3) course design - proposed an introductory course that covers topics to address the statistical skills gap between the academe and industry. To address the component (1), DSA roles were identified and professionals with the identified roles were reached out through purposive sampling and given a survey questionnaire regarding how statistics is used in their organization. A focus group discussion with data science industry leaders was also performed to gain another insight into the statistical skill needs in the industry. For component (2), a review was made on the undergraduate degree programs of data professionals who responded to the survey and on gathered standard course curricula from the Philippine Commission on Higher Education (CHED). A panel of experts in the field of statistics education was formed to map out the correspondence between statistical skills offered by the different undergraduate degree programs, and the needed skills in the industry. The results from (1) and (2) were then used as inputs for (3). The results suggested that among the in-demand statistics skills, descriptive statistics and data visualization were commonly taught while inferential statistics were more often covered by programs from the hard sciences. Also, advanced statistics topics such as time series analysis, regression analysis, and survival analysis courses were only offered in statistics curricula and other closely related fields. Since these advanced topics are offered primarily to statistics majors, these added emphasis on building them on top of a strong theoretical statistics foundation. This theoretical requirement becomes a barrier to entry for non-statistics majors. To address this gap, this paper proposed a sample syllabus to demonstrate how these advanced statistical topics can be taught to degree programs with fewer statistics backgrounds. The syllabus focused on the application of advanced concepts without compromising statistical assumptions and is intended to cover the common use cases from the industry.