Alternate view for investigating data science knowledge gaps
A U.S. perspective
Bibliographic Data
| ID | 21500829 |
|---|---|
| Authors | Delvin Grant (DePaul University), Benjamin Yeo (0000-0003-2790-159X, Seattle University, corresponding author), Binu Jacob (0000-0001-5111-0494, DePaul University) |
| Year | 2025 |
| Publication date | 2025-01-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Information Development (JOURNAL) |
| Journal identifiers | ISSN: 0266-6669 • E-ISSN: 1741-6469 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/02666669241307022 |
| OpenAlex | W4406949199 |
| Language | EN |
| References cited | 31 |
Data science, a dynamic and rapidly growing field with expanding job growth, has wide variations among data science programs and conceptualizations of data scientists’ skills. Consequently, employers face challenges recruiting the right data scientists, due to misalignment between university preparation and industry needs. To explore this further, we present an alternate data science view and use it to investigate knowledge gaps. Text mining of U.S. university syllabi is used to identify knowledge and skills taught by U.S. universities and colleges. These are compared to data science terms and concepts identified in job postings from previous research. We find graduates are better prepared for soft skills, but ill-prepared for technical and analytical skills. The findings are used to discuss how universities and industry can bridge the gaps
Data science · Knowledge management · Sociology · Big Data and Business Intelligence · Computer Science · Information Systems Education and Curriculum Development · Online Learning and Analytics · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |