Addressing The Gender Gap in Data Science

Posted 2019-11-27 Posted by Dorian Martin

Addressing The Gender Gap in Data Science

Data science is a fairly new area in the tech field.

However, it follows an old and negative trend: the underrepresentation of women.

The 2019 USA Diversity in Data & Analytics report by Hanham, for example, surveyed over 1,000 working professionals and found that only 18 percent of data science positions were occupied by women.


Credit: 2019 USA Diversity in Data & Analytics report by Hanham


On top of that, 11 percent of data science teams today don’t have any female members, who also earn less than their male counterparts.

There are different reasons for the gender gap: a lack of female students enrolled in STEM programs (in fact, women represent only 35 percent of all STEM students worldwide, according to reports), a failure of educational institutions and policies to catch up to gender equality regulations, widespread misconceptions about the role of women in the field, a lack of proper mentorship, and many others.

However, an appropriate representation of females in data science matters. For example, this contributes to diversity in the workplace, which fosters innovation, performance, and creativity, as women are excellent communicators, problem solvers, team players, and leaders.

Let’s now take a closer look at the reasons why women are so misrepresented in data science.

Underrepresentation in Data Science: What are the Main Reasons?

Researching the issue of the gender gap in data science points to several distinct issues that were briefly mentioned above:

- A widespread misconception that data science, like many other connected fields, is exclusively for males
- A lack of female STEM students, not only in the U.S. but worldwide
- A lack of proper mentorship for women
- A lack of welcoming workplace culture, e.g. ”boy’s club.”


Issues 1 & 2: Misconceptions about Women in Data Science that Lead to a Lack of Female STEM Students

I decided to combine these two issues because the second respective one is a direct outcome of the first.

So, it’s a known fact that there’s a widespread misconception that fields like science and math are exclusively for men, mainly because they are thought to have a better aptitude for these subjects.

- Most females drop out of STEM programs immediately after they have children, so there’s no point in enrolling. However, studies such as Women in STEM: Myths and Misconceptions show that promotion gap occurs much earlier than that (in fact, it’s years before women have children

- Women are reluctant to enroll in STEM programs. The stats from other fields, including math and social science, reveal an equal balance and representation of women, but the difference in STEM is colossal.

These misconceptions and myths have a profound effect on young women looking to enter a STEM program and build a career in data science. For example, a recent Microsoft study found that young females and girls are deterred from pursuing this career from an early age.


Credit: Microsoft


As you can see, confidence and desire to pursue a career in programming, science, engineering, technology, and science wanes as women graduate from high school and begin exploring career choices. According to the study, only 27 percent of them think that enrolling in a STEM program would be a good fit for them after high school.

One of the most important reasons why young women ultimately choose to pursue other careers is the misconception and myths listed above, the study mentioned. These results often lead to women developing false perceptions of careers in STEM, too.

For example, the study found that 91 percent of the respondents described themselves as creative and 72 percent said it was important for them to follow a career that has a positive impact on the world but only 37 percent of the entire sample thought that a career in STEM - data science included - meets these requirements.

This is completely inaccurate, as a career in data science requires a great deal of creativity; moreover, it’s a known fact that data science holds a great promise for social good and helping to resolve a wide range of humanitarian issues.

It rarely gets better than that.

“To address these stereotypes, a lot of work is required. To encourage the girls to apply for STEM and pursue careers in data science, a collective effort of educational institutions, governments, and businesses is critical”, states Melanie Sovann, a senior content specialist at Studicus.

On top of that, the Microsoft study showed that there seems to be another effective way to bust those myths: skilled mentors.


Issue 3: A Lack of Proper Mentorship for Women

It’s clear that misconceptions and myths are having a strong impact on women and preventing them from becoming data scientists. As the abovementioned Microsoft study discovered, one good way to reduce the impact of stereotypes is to provide girls and young women with positive role models, preferably those already working in data science.

As it turns out, this makes a lot of difference in terms of perceptions of STEM fields as well as the confidence in doing STEM in young women. Take a look.

Credit: Microsoft

As you can see, knowing someone in STEM allowed girls and young women to develop a better understanding of the relevancy of STEM as well as how to pursue a career in a related field. According to the study, more respondents also reported the same because their teachers and/or parents talked with them about STEM and encouraged them to consider pursuing an interest in a related field.

This demonstrates that educating female high school graduates about the relevance of STEM and possible career opportunities and benefits could be an effective approach to increasing the number of female STEM students. This, of course, requires a systematic approach involving local, state, and national educational programs and projects.

Credit: Microsoft

Support from parents and educators thus can be an effective technique for helping female students to develop and/or retain the interest in the field of data science. So, educational programs should be encouraged to share the knowledge of the benefits of pursuing a career in STEM, including data science.


Issue 4: A Lack of Welcoming Workplace Culture

It’s not uncommon for many companies to develop a “boy’s club” culture at the workplace, especially with males holding more jobs than females. Unfortunately, data science is not an exception here. A study conducted by the American Association of University Women, for example, discovered that the percentage of female engineers with high job satisfaction was much lower than those with high job satisfaction.

Credit: American Association of University Women (AAUW)

As you can see, some of the main problems that reduced job satisfaction in the respondents included sexist and undermining behaviors by supervisors and colleagues. As a result, even though 78 percent of hiring managers believe that diversity improves workplace culture, many women still experienced discrimination from male colleagues and bosses.

“In some cases, the workplace culture might even be welcoming and inviting, but women can still refuse to work there,” shares Carly Majri, a researcher from Trust My Paper. “That occurs more often than you think, mostly due to the impressions they have of the field of data science as well as a typical workplace.”

Once again, resolving this problem and breaching the gender gap requires a comprehensive approach. For example, companies play a huge role here, as they can - and should:

- Put more female employees front and center to show that women are welcome
- Foster a workplace culture that promotes diversity and inclusion and eliminates isolation and a hostile attitude toward female employees
- Start diversity and inclusion programs that support women
- Appoint leaders for diversity and inclusion programs and hold them accountable for their performance
- Promote family-friendly policies by offering flexible schedules, home office opportunities, and incentives for childcare. This will help to retain female employees with children.

Also, many women should go after data science jobs regardless of the perception of a typical workplace they have. In many cases, it can be quite welcoming, but misconceptions and perceptions stand in the way of taking advantage of amazing career opportunities.


The Bottom Line

Breaking down the gender gap in data science isn’t an easy task. However, this is something that has to be done, as it increases workplace diversity that benefits everyone. Therefore, it’s critical for all involved stakeholders to understand all the benefits - social, financial, and cultural, among others - and realize that creating a bias - and the discrimination-free workplace is a great way to obtain them.

Dorian Martin loves to write about all things digital. He works for Isaccurate where he’s able to apply all his knowledge and experience. Along with this position, Dorian is a frequent blogger, a marketing and article contributor to a number of businesses that offer digital marketing, AI/ML, blockchain, and data science services to their clients, and provides training to other content writers. He also contributes to his personal business blog and loves every second of it.

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