Rankings vs Realities: Exploring Competency Differences in Graduate Data Science Programs

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of “Are there any differences in graduate data science program competencies based on their university rankings?” The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, Computer Science, Information Science & Technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either “Data Science” or “Business Analytics”; 2) housing the new data science programs within existing departments or schools specializing in Business, Computer Science, Information Science & Technology, and Math/Statistics; 3) incorporating all ACM Data Science competencies into the curriculum of these programs while also emphasizing the need for a strong foundation in mathematics and statistics. Overall, these implications provide valuable guidance for universities seeking to develop impactful and well-rounded data science educational offerings that align with industry demands and equip students with the necessary skills and knowledge.