Fourteen inspiring and influential women who defy the gender gap in Data Science!
Just 15% of today's scientists are women. Like most STEM fields, data science has a daunting gender diversity problem. We need to do something about it.
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Last week, I was having a "career conversation" with my 13 years old daughter; surprisingly, she has shown me a great interest in Artificial Intelligence and Data. I would not define her as a nerd, but she always got excellent grades in STEM.
As a father, I always try to encourage her, mentor her, and support her in the long way to come when it comes to her career and her decision, so I will do what it takes to give her all the possible opportunities to do her experiments until she finds out what it does for her in the life… but I must confess… We must do many things today to support female students' career choices.
There needs to be more diversity: as few as 15% of data scientists today are women. And the lack of diversity is a serious issue. A.I. algorithms are biased, so building them requires a team with a wide range of views and experiences.
Diversity of approaches and viewpoints is critical in building efficient data science teams. For example, machine learning algorithms occasionally "see" patterns that lead to spurious, biased, or even dangerous conclusions. A diverse group must ensure that bias-prone models produce accurate, balanced results. Building such algorithms can be an art as science.
But as explained by one study by BCG — Boston Consulting Group, data science, like most STEM fields, has a daunting problem of gender diversity.
While women make up about 55% of university graduates across countries on average, they account for just over one-third of STEM degrees. (See Exhibit 1.) Only two-thirds of this valuable talent pool embark on a STEM-related career, such as engineering, analytics, and software development, and even less on a…
