The Study of Data Science Lags in Gender and Racial Representation

This article explores the gender and racial disparities in the data science field. It examines the systemic factors behind unequal access to education, hiring discrimination, and workplace culture that inhibit inclusion. Citing data and case studies, the article stresses the need for mentorship, representation, and equitable hiring practices. It advocates for a more inclusive data science ecosystem that supports underrepresented groups and provides actionable solutions to foster diversity, equity, and belonging across educational and professional settings.