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“The biggest challenge for us initially was just, where does data science live? Is it in your math department? Is it in your computer science department? Who's going to teach it? Are you going to have a math faculty? Computer science faculty? And then once you decide where it's going to be, then you have to ensure that you have faculty who are willing to teach, because the class is challenging: it does require some programming, as well as statistical analysis, so it's a lot for a faculty. Usually faculty don't have both of those skills, so that's a challenge.”

In this episode, we sit down with Kyla Oh, Acting Dean of Math, Science, and Career Education at Berkeley City College. Kyla shares her unique path from engineering to patent law and now community college leadership. Together, we discuss the evolving role of community colleges in expanding access to data science education, as well as the challenges that come with building out new programs. Kyla discusses the importance of collaboration across departments and institutions as a means of expanding data science across schools, and highlights the power of support programs and internships to keep students motivated.

“I treat my students like clients. If my students are not showing up to class, then I feel like, oh, I'm doing something wrong. And the same with our industry partners—I want to be able to bring in industry partners, so I have to treat them like clients. Like, how can we best serve you and ensure that that partnership is mutually beneficial?”

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“I feel like most practicing teachers grew up in the same educational system that I did where you are penalized for getting the wrong answer, and you kind of get into this flow of needing to have the correct answer. And that has really informed the way that they teach—they're afraid to be wrong. And so the number one thing I work with teachers on is really building up their confidence to be flexible in the classroom.”

In this episode, we sit down with Hannah Kurzweil, STEM educator and Community Manager for Data Science for Everyone. Hannah shares her unconventional journey to STEM teaching and national community-building in data literacy, and reflects on what it means to support teachers in embracing flexibility and designing interdisciplinary curriculum. Together, we discuss the barriers to bringing data literacy into K-12 classrooms and strategies for building stronger educator communities.

“A lot of teachers feel like they're working in silos…a lot of teachers, often because of the median salaries for educators, don't feel like professionals, and that's really hard when you are so passionate about your work and you don't feel like you're able to be a valued member in society for the work that you're doing. And that's why a community of educators is so important, to bring those levels and that sense of community back, and professionalism back, to the classrooms and the classroom teachers.”

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“At Howard, we're looking at having people understand that data is the new oil, right? Everyone has access to it, everyone should be aware of it, everyone should be able to understand where they fall when it comes to their own data, but not knowing that cost. So we want everyone to kind of have a space to say, I'm not a computer scientist, I am not someone who loves statistics, but I want to get involved in this ecosystem of data science, where can I start? And social impact and social justice is where everyone can find a space to begin to understand why data is so important.”

Today, we sit down with Dr. Amy Yeboah Quarkume—also known as Dr. A—Associate Professor at Howard University and Director of Graduate Studies for the Applied Data Science and Analytics program. Dr. A shares her journey from Africana Studies into data science, and how she’s building Howard into a hub for data science, social justice, and environmental justice. Together, they discuss her groundbreaking projects like What’s Up with All the Bias and the HELLO BLACK WORLD curriculum, the importance of addressing “data pollution” in marginalized communities, and how students of all ages can find their way into coding and data science.

“Let's create more space to make mistakes. And even though mistakes cost—because the environmental impacts of all this…there are impacts to what we do—being able to make a mistake and learn should be something that we should continue to encourage. Coding takes practice, it takes patience.”