Oracle AI & Data Science Blog
Learn AI, ML, and data science best practices

5 Ways To Support Diversity in Data Science

This week, I'm joining 18,000 colleagues and potential collaborators at the Grace Hopper Celebration of Women in Computing, the world’s largest gathering of women in technology. In addition to technical talks and workshops like "IoT for Social Good" and "Mission Critical Computing Systems for Space Flight," taking center stage are sessions addressing a critical lack of women in STEM fields, including “Why Has Tech Failed at Building Diverse Workforces?” and “Strategically Developing and Retaining Women in Leadership.”


When I started my career as an electrical engineering student at Stanford over 15 years ago, I didn’t really know what to expect in terms of diversity — or a lack thereof  — in my field. It was only when I finished my PhD and joined a cohort of civic-minded AAAS Science & Technology Policy Fellows that I had an opportunity to work as part of a technology team that had more women on it than men. I experienced a similar "Wow" moment during my first Grace Hopper conference last year where the presence of 15,000 enthusiastic women in computing underscored the power of role models, mentoring, and a community that is intentionally inclusive.

But Grace Hopper only happens once a year, and the fast-moving ecosystem of data science, data analytics, and data-enabled technologies is at a critical juncture. Arecent analysis of thousands of students from General Assembly’s 20 campuses noted low enrollment from women and Latino and African American students in data science compared to other courses, and reports continue to show low diversity in the STEM workforce. This is especially concerning given that other studies correlate diversity with business success and recognize that gender parity can be one of the most effective ways to raise rates of economic growth.


The University of California at Berkeley is proactively shaping the trajectory of the data science field with its new Division of Data Sciences, which has been designed to be inclusive from its inception. The Foundations of Data Science course grew from  300 students in Spring 2016 to over 1,000 this semester, making it the fastest-growing course in the university’s history. Notably, the course’s gender ratio is regularly close to 50/50, with the number of female students sometimes even tilting that scale. The university takes a zero-barrier-to-entry approach to the class, teaching examples are carefully chosen, and a diverse team of students is tasked with actively recruiting across campus.

As I sit here surrounded by thousands of innovative women, I wonder: How might we take inspiration from successes like Berkeley's data science program and the Grace Hopper conference to better support diversity in data-related fields? There are many answers to that question, but I'd like to offer five ways to start making a change today.

Have a Diversity-in-Tech Story in Mind

Find three awesome people who are creating value — and who happen to represent an underrepresented minority. Share these stories to inspire others and integrate these successes into our community fabric. When I was starting out in science, my mother told me about Marie Curie pioneering research in radioactivity, and I saw firsthand that my mom was a successful inventor and technology leader. Both of their stories inspired me to become who I am today.

Don’t know where to start? The Untold Stories of Women in STEM offers short podcast episodes on inspiring women. You can also check out #GHC17 on Twitter this week for more stories.

Keep it Real  

Is your point of inspiration someone you know well? Did she create something that significantly impacts your daily life? Identify a role model or source of inspiration that is directly connected to your network. Potential collaborators are everywhere — check out the participant list for the ACM Richard Tapia Celebration of Diversity in Computing conference or information on the 250+ sessions at Grace Hopper this year for real-world examples of leaders with diverse backgrounds.

Mentor Generously

In speaking with my fellow "Treading Water in a Sea of Data: Practitioners' Perspectives" panelists at Grace Hopper, one common experience we shared was the guidance of an excellent mentor, either during a hands-on internship or through regular work experience. Being a mentor and having a mentor are both vital to building confidence. You can pay it forward throughout your career by sharing your expertise — and lessons learned — with others.

Say “No” — Generously  

Next time you decline an invitation to speak or serve as a board member, consider recommending someone in your place who is from an underrepresented group. Research from theBoardlist shows that this year, men held over 90% of board seats for companies valued over $1 billion, and it's not uncommon to attend tech conferences that feature all-male panels. Groups like TechWorld and August, as well as programs like the 50-50 pledge, are gathering names of tech speakers and leaders to help make it easier for anyone to find qualified candidates who are also women or minorities.

Lead by Example

Whether you’re overseeing a project, hiring an entire team, or recruiting volunteers for an event, make an effort to ensure that your group is every bit as diverse as your intended audience or community. The more we practice what we preach, the more inclusive our fields will become. 

What are other ways we can help support diversity in data-related activities and initiatives? What data about diversity is missing from the conversation? Share your thoughts with the Regional Big Data Innovation Hubs and our collaborators by using #BDHubs on Twitter. Thank you in advance for your contributions to a more inclusive community. 

About the West Big Data Innovation Hub's Work With DataScience.com

The West Big Data Innovation Hub continues to partner with DataScience.com for various initiatives, ranging from a workshop series on data-driven storytelling to the National Transportation Data Challenge. Meet Meredith and colleagues at DataScience.com headquarters on Oct. 12 for the “AI For Good: Big Data Challenges for Disaster Response and Recovery” PyData meetup to learn more about the tech resources that DataScience.com and partners are making available.

Like this content?
Check out the video of DataScience: Elevate - Spotlight: Women in Technology. 


Be the first to comment

Comments ( 0 )
Please enter your name.Please provide a valid email address.Please enter a comment.CAPTCHA challenge response provided was incorrect. Please try again.