Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence

TL;DR

This study uses text mining to analyze 41 Dutch master programs in data science and AI, revealing key skills taught and differences between university types, aiding stakeholders in understanding data scientist training.

Contribution

It applies Correlated Topic Modeling to identify and compare core skills across Dutch data science and AI master programs, highlighting differences between university types.

Findings

Research, data processing, statistics, and ethics are predominant skills.

Research universities emphasize research skills; technical universities focus on IT and electronics.

Skills vary significantly between university types.

Abstract

The growing demand for data scientists in the global labor market and the Netherlands has led to a rise in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skillsets of data scientists. This study aims to address this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by seven Dutch universities. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our findings reveal that research, data processing, statistics and ethics are the predominant skills taught in Dutch data science and AI master programs, with general universities emphasizing research skills and technical universities focusing more on IT and electronic skills. This study contributes to a better understanding of the diverse skillsets of data scientists, which is essential for employers, universities, and prospective students.