Read on to learn more about the challenges women face in the field of data science, and how current female data scientists are creating opportunities to help bridge the gender gap in STEM fields.
Here’s what we’ll cover:
It’s no secret that STEM professions—shaped by years of gender and racial bias—lack diversity. Data science is no exception. A survey conducted by the Boston Consulting Group found that roughly 15-22% of data scientists are women, and that organizations continue to come up short in attracting and retaining women employees, even though the talent is there.
But progress, although slow, is being made. Companies are waking up to the fact that lacking diversity doesn’t just look bad, it adversely affects the bottom line. Without diverse and balanced data science teams that include women at all levels, algorithms can skew toward the biases of the dominant group, interpretations of data insights don’t have the benefit of different points of view, and companies building products and services for everyone end up with glaring blind spots.
In addition to companies making a greater effort to build and support a diverse workforce, many industry leaders have taken matters into their own hands, creating opportunities and support networks for women in data science and clearing some of the hurdles that stand in the way.
One of the main challenges companies face in hiring more women data scientists is that the profession itself has an image problem, according to the Boston Consulting Group, which can deter women from training to become data scientists, to begin with. The image problem is fueled by a number of myths that researchers believe can be overcome.
Every facet of data science, whether it’s data gathering and cleaning, data analysis, forecasting, or machine learning, benefits from the input of diverse teams. Recognizing some of the barriers to entry that can deter women from careers in the profession, data science leaders have launched conferences and initiatives to support women from the time they’re in school through to when they’re in the workforce.
Some of the more notable conferences, organizations, and networking opportunities include:
A growing number of organizations have thrown their support behind helping the data science industry achieve proportional representation, namely through offering scholarships, fellowships, and grants. Data science scholarships are available for undergraduate and graduate students who are pursuing data science or related fields, and there are also paid internship programs that aim to give students from historically underrepresented groups hands-on industry experience.
On the online learning/bootcamp front, Springboard has partnered with Women Who Code to offer ten scholarships worth $1,000 each to women who enroll in Springboard’s Data Science Career Track, Software Engineering Career Track, or the Machine Learning Career Track.
Springboard also offers a number of women-in-tech scholarships.
Students who have completed Springboard’s Data Science Career Track have gone on to work as data scientists at companies like Boeing, Johnson & Johnson, Pandora, and Amazon.
Many graduates of the program credit Springboard’s three-pronged approach to their success:
“My first meeting with my mentor, Ike, set the tone for a very productive relationship,” said Sara Weinstein, a Springboard graduate who is now a data scientist at Boeing. “Several hours later, I got an email from him with a long list of resources tailored specifically to what I’d told him I wanted to learn. I was absolutely flabbergasted—it must have taken him at least an hour. Here was this total stranger who had taken the time to identify a whole bunch of books, PDFs, and papers just got me, that I would not have found on my own. It was just amazing!”
In addition to mentorship from an industry expert, Springboard’s curriculum is designed to be an accessible, guided learning experience that makes data science training less overwhelming.
“I had several false starts trying to teach myself R and Python because there were too many resources out there,” said Arti Annaswami, a Springboard graduate who founded her own data science consultancy. But once she started Springboard’s Data Science Career Track, she found that “it’s amazing how frictionless the learning process becomes when you’re only looking at the best material out there for a specific topic, curated by folks who are industry pioneers in that field. By the end of the course, I felt fully comfortable doing a start-to-end data workflow in R.”
Despite the poor diversity numbers in the field of data science, women occupy some of the most influential and pioneering roles in the profession. A few industry leaders include:
“The biggest piece of advice I have for anyone is to ask for help,” Emily Bailey, a data scientist at Uber told Springboard during a panel on women in tech. “When you have a goal in front of you, it can be daunting to see how big it is, so the first place I tend to ask for help is: how do I break this up into smaller pieces? And then, even if the first piece is hard, you can ask for help every step of the way if you need to.”
“You don’t have to check every single box,” Kathy Yang, a data scientist on Airbnb’s strategy and insights team, added. “I think especially in data science, which has such a broad scope, no one’s going to be an expert in everything. So there’s always going to be things you don’t really understand, or that you can do better.”
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