Using Natural Language Processing to Correlate University Curricula with Required Job Skills

Aligning academic curricula with industry demands is essential for preparing graduates with relevant job-market skills. This study examines the alignment between our University’s Department of Electrical and Computer Engineering (DECE) curriculum and industry job requirements across five thematic areas: Communication Systems, Computer Systems Engineering, Control Systems, Electronic Systems, and Energy Systems. Using Natural Language Processing (NLP), we extract and analyze key skills from course outlines and job postings. Web scraping with Selenium and BeautifulSoup gathers job descriptions, while SkillNER identifies required competencies. We then used Latent Dirichlet Allocation (LDA) topic modeling to assess thematic alignment. Findings reveal that, while core technical concepts are well-represented, emerging technologies and software tools are not. Alignment varies across thematic areas, hence the need for curriculum updates to better reflect industry expectations.