Women in Data Science

This was from a recent FB post (Stanford School of Engineering, #IAmAnEngineer series). Comes with a pic of yours truly, but that's not important here.

School of Engineering: "We need more women in data science. As the field continues to grow and have an increasing impact on industries from healthcare to environmental sciences, we’d be remiss to leave 50% of the talent on the table. No one believes this more than Margot Gerritsen, director of the ICME: Institute for Computational & Mathematical Engineering at Stanford. Which is why she set out to highlight and celebrate Women in Data Science by organizing a conference that boasts an agenda of all female data scientists. The second Women in Data Science (WiDS) conference takes place Friday, February 3. See the full agenda here: https://www.widsconference.org. Recently, we sat down with Margot and four Stanford alums to hear more about their passion for supporting women in data science as well as what it means to each of them to be an engineer."

Margot Gerritsen: "#IAmAnEngineer and I am passionate about getting more women into this exciting field. In today’s world, more and more decisions are being driven by data. Management decisions, political decisions, industrial decisions, commercial decisions, environmental decisions, healthcare decisions – the list goes on and on. As a result, data scientists are becoming increasingly influential. And yet, when you look at the composition of data science teams, most of them are men. There’s very little diversity. And then you may say, Who cares? These are scientists and engineers that look at the data very analytically; bias doesn’t come in. But that’s wrong. Data is very malleable. Bias can creep in quickly. Diverse teams often probe data in a different way and ask different/additional questions, and they may come to different conclusions. When I think about a world where data scientists have a lot of impact and influence, I want data science teams to be much more diverse, and to be more representative of the world’s population. Having more women on these teams is just logical.

Also, there’s incredible talent among women. Why would a company, a nation, or an academic institution not try to tap into that talent pool? Why would anyone be satisfied leaving that talent on the side, and saying, OK, fine, we have 10 percent, 15 percent women, that’s good enough. And this immense untapped talent doesn’t just exist in women, there’s also tremendous talent among underrepresented minorities, and the more of these people we have in the field, the better.

It’s not hard to see that we don’t yet have a culture in data science and engineering that successfully attracts and retains minorities. They feel discouraged or perhaps not well supported and may feel as though they don’t belong in this field. This is a cultural problem, not a genetic problem, and we have to do something about it. The Women in Data Science conference started because of a strong desire to change our culture, to inspire young women to enter the field and to do so by celebrating and showcasing incredible women in the field of data science.

I’ve been in computational engineering for almost 35 years, and I’ve very often been the only woman, or one of only a very few. When I first entered this field, it was made up of about 15% women. I was sure that by 2017, the percentage would be higher, but it’s not.

A lot of young women tell me that they fear they’re not as good as others. They have imposter syndrome, as we call it – this feeling that they don’t really belong, and that they cannot possibly live up to expectations and do not have the natural talent to be successful. A lot of women still feel that mathematics is an innate ability, but I absolutely do not believe this is true. In other words, a lot of women – and men! – think that you’re born with an ability to do math, that you can either do it or you cannot. This is a fixed mindset. We must move beyond a fixed mindset to develop a growth mindset. Instead of believing your abilities are fixed, build your eagerness to learn, your belief that even with setbacks, and even with occasional failure or disappointment, you can continue progressing and learning from all of your experiences. Instead of being anxious about struggles, I’ve learned to turn my thinking around and say, “This is wonderful. I don’t know enough, I’m struggling, I understand that. But that means I’m learning, it means I’m stretching myself.”

And so, I tell my students, I became comfortable being very uncomfortable, and now I look for that feeling. Now I want to feel slightly off. I don’t want to be super-comfortable, because that means I’m not learning, I’m not stretching myself. And so, when I start feeling insecure and uncomfortable, I check with myself, and say, “OK, good, I’m learning.” And if I fail, that’s fine too, it's all part of the learning process.