What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities.

The aim of this study is to use user-generated content from online Q&A forums to systematically analyze the common issues and points of discussion in the field of data science. The methodology centers around the use of advanced semantic content analysis to help identify, classify, and comprehend the issues that data scientists most commonly face and discuss. Through the analysis of massive amounts of unstructured data from problem-and-solution-oriented data science communities, this research attempts to document the problems, trends, and issues that recur in the practice of data science. The proposed methodology involves the collection of data from specific Q&A forums, followed by extensive text preprocessing, including, but not limited

to, tokenization, stemming, and removal of stop words. Then, specific semantic analysis methods such as theme identification and issue-centered keyword extraction will be implemented to help elucidate the major issues and themes in the data. This study aims to outline the key issues and challenges in the field of data science to aid in the development of relevant curricula, the improvement of research tools, and guidance for future research in the field. The results are expected to reveal to stakeholders the real challenges experienced by data scientists to help facilitate the discipline’s advancement.

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