Let's Make Gender Diversity in Data Science a Priority Right from the Start | Zendy

Francine Berman | Zendy; Philip E. Bourne | Zendy

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Let's Make Gender Diversity in Data Science a Priority Right from the Start

Author(s) -

Francine Berman,

Philip E. Bourne

Publication year - 2015

Publication title -

plos biology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 4.127

H-Index - 271

eISSN - 1545-7885

pISSN - 1544-9173

DOI - 10.1371/journal.pbio.1002206

Subject(s) - diversity (politics) , workforce , gender gap , women in science , biology , set (abstract data type) , field (mathematics) , engineering ethics , data science , focus (optics) , gender diversity , inclusion (mineral) , science and engineering , data set , public relations , computer science , sociology , political science , social science , management , engineering , gender studies , mathematics , artificial intelligence , anthropology , law , demographic economics , optics , programming language , physics , pure mathematics , economics , corporate governance

The emergent field of data science is a critical driver for innovation in all sectors, a focus of tremendous workforce development, and an area of increasing importance within science, technology, engineering, and math (STEM). In all of its aspects, data science has the potential to narrow the gender gap and set a new bar for inclusion. To evolve data science in a way that promotes gender diversity, we must address two challenges: (1) how to increase the number of women acquiring skills and working in data science and (2) how to evolve organizations and professional cultures to better retain and advance women in data science. Everyone can contribute.

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