Identifying Relevant Digital Skills in Collaborative Network of Jobs Using Eigenvectors and Eigenvalues Analysis
摘要
This paper analyses the principal components within collaborative networks of digital skills, jobs, used technologies, and tool providers. By applying advanced techniques such as PCA (principal component analysis), the scope is to identify the most relevant digital skills related to job needs, the primary tool providers aligned with the utilized technology and digital skills, and the technologies exhibiting significant growth. In the end, by analyzing all results as a collaborative network, a map can be created that illustrates the connections among the most relevant digital skills, jobs, technologies, and tool providers. This study illustrates a practical application of PCA techniques to identify trends and connections between digital skills, job needs, tool providers, and technological advances. The findings may serve as a foundation for identifying the professions and skills that require substitution, removal, or enhancement due to the transition to the new digital realities.