The Application of LDA Model in the Analysis of Job Talent Demand under Big Data Technology

Wei, Jize; Xu, Yaxi

doi:10.1109/aie57029.2022.00065

Cited by 1 publication

(1 citation statement)

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“…Text clustering is a common method in text mining technology and is widely used in many fields, including topic discovery and hotspot tracking. Some studies have extracted high-quality information through data processing and further refined concepts related to the occupation in question using LDA [15], [26], Word2vec [27], [28], BERT [28], and TF-IDF [29] to understand the dynamics of the labor market.…”

Section: Big Data Job Market Trendsmentioning

confidence: 99%

Analyzing Big Data Professionals: Cultivating Holistic Skills Through University Education and Market Demands

Han,

Ren

2024

IEEE Access

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This study investigates the alignment between the education of big data professionals in universities and the requirements of the labor market. Through the examination of data extracted from Chinese job advertisements, this study explores the multifaceted skill set sought by employers in the big data domain, with an emphasis on achieving a harmonious equilibrium between technical ("hard") and soft skills. Employers expect a high level of proficiency in data processing and analysis coupled with capabilities in teamwork, communication, and leadership. The dynamic nature of big data, characterized by rapid technological advancements, underscores the importance of continuous learning. The demand for big data talent has various roles, including big data scientists, engineers, and workers, thus contributing to a diverse and competitive job market. Recognizing the potential gap between university education and the dynamic requirements of the job market, this study advocates the integration of practical skills with theoretical knowledge. It proposes that universities assert their autonomy in curriculum design while concurrently engaging with alumni and industry partners to afford students' real-world experiences. In conclusion, this study underscores the significance of cultivating a sustainable talent development system. This encourages a curriculum that adeptly balances timeliness and innovation, producing graduates armed with contemporary tools and a comprehensive understanding of data science principles. This approach fosters versatile professionals capable of effectively addressing the myriad challenges within the diverse landscapes of the industry.

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