Based on LDA Topic Model: An Analysis of Talent Demand and Competencies for Data Analysis Positions

To deeply explore the specific demand characteristics of data analysis positions in the current job market, thereby providing precise talent recruitment guidance for enterprises and clarifying career development paths and key competencies that need to be enhanced for job seekers, this study focuses on analyzing the relevant competencies required for data analysis positions. Using web crawling technology, a total of 979 job postings with the keyword “data analysis” were collected from the recruitment website “51job,” and after data cleaning, 717 valid data entries were obtained. The Latent Dirichlet Allocation (LDA) topic model was employed to identify and analyze the topics within these data, resulting in the extraction of nine related job demands based on topic intensity, including basic data analysis and modeling, data support and decision-making reporting, as well as business operation analysis. The research conclusions indicate that the demand for talent in data analysis positions is diversified and specialized, requiring practitioners to not only proficiently master core technical tools such as programming, databases, and statistical analysis but also possess business understanding combined with industry knowledge, cross-domain integration capabilities, and excellent soft skills and team collaboration abilities. This paper utilizes the LDA topic model to analyze the demand for talent in data analysis positions, aiming to provide certain references for relevant talent training institutions, thereby contributing to better meeting the demand for data analysis talent in the labor market.