WHAT'S WRONG WITH THIS PICTURE?
While these visualizations show graduate programs remain dominated by White and Asian males, the data is not the full picture. The four main issues with our work:
INTERSECTIONAL IDENTITIES ARE NOT INCLUDED
The data used in these visualizations do not come close to including all the measures of identity.
Image credit: YW BOSTON
The Field Is Messy
Data fields overlap and programs vary widely. Unlike more traditional departments, like computer science and information systems, there is no national standard designation for data and data science. Because of this, our data sources vary, creating inconsistencies.
DATA PROGRAMS ARE GROWING RAPIDLY
Because data science is such a high-demand field, degree programs and other data science courses are popping up all over. This means that demographic data quickly becomes obsolete. Because the number of people studying data science is growing so quickly, it is important to have the most current data.
THE DATA IS LIMITED
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Publicly available datasets rely on institutional reporting. Meaning, we have to use data that institutions submit. In the case of the Taulbee survey, only institutions offering PhDs in computer science, information science, and computer engineering are invited, and they are not required to participate.