Mutually Beneficial Collaborations to Broaden Participation of Hispanics in Data Science

Patricia Ordóñez Franco
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Abstract

Representation of Hispanics, especially Hispanic women, is notoriously low in data science programs in higher education and in the tech industry. The engagement of undergraduate students in research, often and early in their path towards degree completion, has been championed as one of the principal reforms necessary to increase the number of capable professionals in STEM. The benefits attributed to undergraduate research experiences have been reported to disproportionately benefit individuals from groups that have been historically underrepresented in STEM. The IDI-BD2K (Increasing Diversity in Interdisciplinary Big Data to Knowledge) Program funded by the NIH at the University of Puerto Rico Río Piedras (UPRRP) was designed to bridge the increasing digital and data divide at the university. The college's population is 98 percent Hispanic and yet there is no formal data science program. There also exists a gender imbalance in computing at the College of Natural Sciences at the UPRRP. Over 60 percent of the undergraduate students in Biology are women. However, the percentage of women in Computer Science hovers around 15 percent. The IDI-BD2K was created to address both these concerns and increase the participation of Hispanics in interdisciplinary computational and quantitative research. In this talk, I will highlight the need for mutually beneficial university collaborations to reduce the digital and data divide, create greater awareness of the growing disparities and increase the number of future faculty with experience teaching diverse students.
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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021 Mutually Beneficial Collaborations to Broaden Participation of Hispanics in Data Science Bringing Inclusive Diversity to Data Science: Opportunities and Challenges A Causal Look at Statistical Definitions of Discrimination
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