This is FiveMinuteFriday episode number 196, diversity in data science. Welcome back to the Super Data Science Podcast. Ladies and gentlemen, super excited to have you on the show today.
This week, our guest on the podcast was Gabriela De Queiroz. If you listened to the episode, then I’m [inaudible 00:00:22] and sure that you enjoyed it because we had a wonderful chat with Gabriela. Today, I wanted to add some numbers to add more color to the conversation we had to one of the topics that we covered. One of those topics was diversity in data science because Gabriela, being the founder of rladies.org has a mission to help women get into the space of data science, feel that it’s a great and friendly environment where they can boost their skills and progress their careers.
Today, I actually found this article on KDnuggets, which is called Diversity in Data Science: Overview and Strategy. It’s by Colleen Farrelly. You can the it there. It’s very recent, or we’ll link to it in the show notes at
https://www.superdatascience.com/podcasts/diversity-in-data-science. Let’s put all this into perspective. All the things that we talked about, diversity. Let’s look at the numbers. What is actually going on? Here we go. Approximately 33% of the tech industry is femaleminorityand approximately 16% is underrepresented minorities, Hispanic or African-American. Management, on the other hand, is approximately 5/6, which is, what is that? 84% Caucasian.
Large tech companies report even lower numbers, so now, we’re going even deeper into the situation. Companies like Google and Apple report even less diversity. About 24% are women, and only 5% are underrepresented minorities. Now, if we go even further, on development teams, women take up as few as 13% of roles and women in technical positions are twice as likely to leave a position as men. As you can see, the numbers are extremely, extremely staggering and speak for themselves.
Now, the article goes on to talk about the benefits of having a diverse team, and the benefits are undeniable, gave some examples of companies growing and exceeding their peers and getting boosts in their likelihood of succeeding. You can find all that in the article. I won’t go into detail there. We can see that the numbers are staggering, and at the same time, there are huge benefits to it. The question is, why is there this gender gap? Why is there this diversity gap?
To answer that, we have to address, look at where the situation starts. The situation doesn’t start at the leadership positions, that women are not just being hired into leadership roles in the space of data science. It actually begins at the very beginning at the onsets when people are actually learning about data science. What the article talks about is that a person is less likely to start learning data science if they see few people who they can relate to already in the field. Even with the advancement of online training programs like MOOCs and online courses, which are free or paid courses, even then, there is still a gap that doesn’t fully bridge the participation gap, so what can we do?
Well, we can just play our parts. I’ll tell you how we are playing our part. For instance, for example, at Super Data Science, at our conference, Data Science GO, we actually have a panel on women in data science. We’re inviting Gabriela De Queiroz. We’re in other female data scientists to speak about these issues to address them and to help women who are participating, who are coming to the event to see that, yes, they can have a successful career. Yes, there are women who have achieved greatness in the space of data science. To popularize data science across underrepresented minorities, across different genders, not just focus on what is the status quo right now, we need to change the status quo.
That’s how, for instance, we’re playing our part. How can you play your part? Well, you can, for instance, lead by example. If you are a female or if you are Hispanic or African-American, you can lead by example and demonstrate that you are building a career in the space, and help encourage other people especially students to get into the space and show them that they are people that they can see and get inspired by. On the other hand, you can encourage, even if you’re not a female, Hispanic or African-American, you can encourage your colleagues and friends or people you know to get into the space.
There are so many courses online. If you’ve taken a course that you like, a paid course, a free course, it doesn’t matter. If you know some resource that you like and you know somebody who’s kind of like hesitating about getting into the space of data science or might be interested in the space, send it to them and help them learn more. Encourage them to learn more and know that they will be successful if they put in the time and effort to develop their skills and progress their career.
That’s how we can help and how we can address this gap. The main point is that a lot of the publications and so on, they look at, at the top echelons, they look at the leadership positions and saw what’s happening up there in terms of diversity, but we need to start early. The good news is that anybody can contribute to that. All of us, we can help people who are starting out in data science feel encouraged, feel empowered, feel that this is a friendly space where anybody can succeed regardless of their gender or race.
There we go. That’s a little excurse into the world of diversity in data science. Make sure to play your role in this space, and we can build this amazing, amazing community, amazing profession out, all of us together so that it is beautiful in the years to come. On that note, thanks so much for being here today. Hope you enjoy today’s episode. I look forward to seeing you back here next time. Until then, happy analyzing.
