Why aren't there more women in data science?

We don’t see an equal representation of women in data science, and here’s why

Why aren't there more women in data science?

When I walked into my first data science class at university, there was one thing that stood out to me more than anything else. The lack of female students in the class.

There were only around five girls in a class of 20 students, which I found pretty strange. My classes at school had approximately an equal number of boys and girls, and this was the first time I found myself in a male dominated environment.

After being in the field for a couple of years, I am now used to it.

Most of my classmates, lecturers, and advisors are male.

The field is so male dominated, it is always a surprise for me to see a new female student or lecturer. It isn’t just data science, but most tech spaces such as clubs, societies, and workshops that are predominantly led by males.

Most of my female friends who fared well in subjects like math and science shied away from any STEM related field. The ones who did want to pursue a STEM related career didn’t even want to consider working in technology or engineering, let alone data science.

As I progressed through my data science journey, there was one thing that had started to become increasingly clear to me.

The STEM field has a diversity problem.

On an average, women make up around 55% of all graduates across countries.

Yet, they account for only one-third of STEM degrees.

World Economic Forum, The Global Gender Gap Report 2018

As seen in the figure above, this number further decreases when it comes to fields like analytics, software development, and engineering.

And only a fraction of these women make it into careers related to data science and artificial intelligence.

The Problem

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I have had conversations with many people on the lack of diversity in the tech industry.

While some agree that there is a diversity problem, others argue that the rise of an individual in a scientific field depends solely on their capability and inclination towards the subject.

Whenever the subject of encouraging women to pursue a career in the sciences is brought up, it is shot down by the following statements:

Girls are less inclined towards STEM fields, their minds are not as technical.

There is an equal opportunity for women in tech fields, whether or not one rises in the field is solely based on their ability.

Women don’t need to be encouraged to pursue a career in the sciences, they should be encouraged to do what they want.

While I agree with the last statement — that every individual is inclined towards different things and should be encouraged to pursue a career in whatever makes them happy, there is a deeper issue here that needs to be addressed.

From a young age, girls are being actively discouraged from pursuing careers in the tech industry.

It is my belief that if the playing field was truly levelled, we would be seeing a lot more female representation in the industry.

The problem starts at home — with the mindset by which girls are raised, to the lack of role models in a male dominated field.

In this article, I will shine light on a few reasons we don’t see an equal representation of women in STEM related fields such as data science.

Why is there a lack of diversity?

Girls are not naturally less inclined towards pursuing a career in technical and scientific fields, and there is data to support that.

In primary school right until their early teenage years, approximately 74% of girls express a desire to pursue a career in STEM.

This interest starts to drop around the age of 15–16. By the time they graduate high school, there are even fewer girls looking to study a STEM related field.

This number reduces so much, that by the time they graduate university, only 19% of all women hold STEM related degrees.

So if girls are enthusiastic and enjoy subjects like math and science when they are younger, why does this interest start to drop during their late teenage years?

The way girls are conditioned

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There is a difference between the way girls and boys are conditioned. When they are young, boys are often risk-takers. They are the adventure seekers, who are allowed to fall and get their hands dirty. There is always room for making mistakes, and getting back up.

On the other hand, girls are conditioned to be perfect. There is no room for error. Even in school, they tend to outperform their male peers. Perfection is often expected from girls, who are not allowed to take the same risks that boys do.

They are more likely to stick to the rules, follow instructions, and get good grades.

However, pursuing a career in a technical field is completely different. In fields that require programming, the only way to learn is to fail.

For someone raised to cram from textbooks and get perfect grades, this isn’t something they are used to.

The only way to learn in a field like data science is to make mistakes. After trying again and again, you will see a miniscule amount of progress.

A person who wasn’t raised to be constantly perfect in everything they do, would understand this. They are used to failing and trying again and again until it works.

However, the perfectionist doesn’t know this.

The perfectionist doesn’t know how to fail, and the very taste of this experience can be overwhelming.

This is also the reason we see some of the class toppers at school completely break down when they go to college, while the below-average students outperform them.

Of course, this conditioning and pressure to be perfect doesn’t apply to every girl, and it does happen to boys too. However, a larger amount of girls face the pressure of perfection at a young age.

It is this pressure that later turns into the fear of failure, and this fear of failure that puts them off technical fields.

The lack of female role models in the industry

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Babies learn to walk by imitating people around them. Even as children, imitation is a quality that helped us learn and master new skills.

We learn by following in the footsteps of people before us.

If someone has done it before, then we can definitely do it too, right?

Unfortunately, the tech industry is one that has always been primarily male dominated. When we don’t see programmers or data scientists who look like us, we tend to shy away from the field.

Since it is so rare to find female role models in the industry, girls rarely have someone to look up to. This puts a lot of girls off pursuing a career in STEM fields.

But… we don’t meet the requirements

When it comes to tech or data science, most job postings are downright ridiculous. There is no way for a fresh graduate to master the technology stack listed in the job requirement.

In this area, men have the upper hand as compared to their female counterparts.

Women only apply for jobs when they feel like they meet 100% of the job requirement. On the other hand, men apply when they think they meet just 60% of the requirement.

This gives men an upper hand, as they are so much more likely to receive a job offer.

The reason that this happens is again due to conditioning. Men are willing to apply despite knowing they may not have the required expertise, since they are willing to take the risk and learn the skills on the job.

Women, on the other hand, want to wait until they are fully qualified to apply. This is almost impossible, especially when it comes to the tech industry.

A lot of skills are picked up along the way on the job, and it is impossible to meet every requirement/know every tool being used.

Is there a solution?

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There is a lot of work being done today to create space for women in tech and data science. There are organizations actively working to help women break into the tech industry, and help them find jobs.

There are employers seeking to recruit data scientists, who work to ensure that their applicant pool has a balance of both male and female candidates.

However, most of the work being done to help women in data science is done at a university level, and once they graduate.

By this time, there already is a very small percentage of girls pursing majors in these fields.

As explained above, we start losing women looking to pursue career in STEM in middle school, and later on at high school.

If we truly want to see an equal representation of women in tech, we need to start by encouraging girls to pursue these fields at a school level — when they are around 15–16 years old.

It is also incredibly important to change the way girls are conditioned. Girls should be encouraged to take more risks, and make mistakes. They need to understand that perfection and getting straight A’s is something that can be left behind at school.

When entering college and the workforce, the only way to learn is by doing. By making mistakes, falling over, and then getting up again.

The only way to create a truly inclusive space for women in technology and data science is to encourage them from high school, and help retain their interest in the subject by allowing them to make mistakes.

It is also incredibly important that we have more female role models in the industry. We grow and learn by imitating people around us, and people who were there before us.

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The media needs to do a better job at breaking stereotypes. Growing up, kids see stereotypical depictions of men and women everywhere — in books, movies, and ads.

This will stick with them for life, and continue to have a huge impact on their life choices and career aspirations.

Women need to be seen taking on more roles in tech industry, both in the media and in real life. This will allow young girls to envision themselves in these positions, and develop an interest in STEM related fields.

In conclusion, the lack of female representation in data science has nothing to do with ability or inclination. For years, women have been actively discouraged from pursuing a career in the sciences, and data science is no different.

If we want to see more women in tech-related fields, we need to change the way girls are conditioned. Teenage girls should be encouraged to pursue careers in tech, and they should grow up watching female role models in the field.

Organizations need to ensure that there is a balance in the talent pool, and address career progression challenges women face. Female workers need to have an equal opportunity to grow within the company.

At every stage of life, girls should be encouraged to pursue a career in STEM. Mentorship programmes should be conducted — from school outreach to late career support.

Through these schemes and support at every stage of their vocation, women can start to grow, and obtain a successful career in STEM. This way, we will see more women represented in the field of technology and data science.