Women make up roughly half of the planetary population, and as such, half of the global workforce. Why, then, are there so few women in data science and related fields?
In the early 1990s, women made up 35% of the data science workforce. Today, they are holding fewer than 26% of available jobs.
Here’s why so few women are choosing this growing industry to start their careers and what can be done to encourage them.
A History of Women in Data Science
In spite of the apparent gender gap, women have been a part of the data science industry from the very beginning.
Ada Lovelace wrote the first computer program in 1837 for the theoretical computer called the Analytical Engine, designed the same year by British inventor Charles Babbage. Lovelace died before the Analytical Engine could be built, but she is often regarded as the mother of modern programming.
Grace Hopper, a Navy rear admiral and computer scientist, developed one of the first high-level programming languages — COBOL — which is still used today.
Women have been part of data science since the first computers were thought of as advanced when they could do three addition problems a second.
What is it like for women in the field now, where amazing role models have paved the way for them?
Tech, in general, is still thought of as a boys-only club. Men hold the majority of the jobs in these industries. This gender disparity isn’t restricted to data science, though 54% of women in tech report men and women aren’t treated equally.
It’s harder for women to break into the field and thrive there, which means there are plenty of job openings and not enough skilled workers to fill them.
In 2012, the Harvard Business Review called data science the sexiest job of the 21st century. While this may still be true, seven years later, women can’t seem to catch a break in the field. In a 2018 interview, two female data scientists quantified why they struggle to succeed.
One, named Jennifer Schaff, said the makeup of the work environment plays a huge role in whether or not she succeeds in an office. “Discussions about common interests lead to friendships and collaborations that are harder for a female to establish,” she says. This lends more credence to the “data science being a boy’s only club” argument.
The other, LeAnna Kent, has found it difficult to get her colleagues and peers to trust her skills, even when she’s right. “After submitting a code rework that implemented abstracted functions to improve the modularity of a script, I was asked who helped me write my code,” she says.
All the data scientists interviewed believe there are increasing opportunities for women in this field, but there are more than a few challenges for these enterprising people to overcome.
The Future of Women in Data Science
What do these challenges mean for the future of women in data science? It proves they have many hurdles to overcome before the field will truly be equal — and that can be said of most industries, even those that aren’t primarily male-dominated.
It will take persistent and tenacious women to turn the field of data science from a boys-only club into somewhere that welcomes and embraces skilled female data scientists.
That might not happen for a while, but if schools and mentors start encouraging young women to embrace their skills and interests rather than urging them to pursue more traditional female careers, there could be a massive industry shift in the future.