Teaching Data Science in a Changing World: Judith Canner on Reform, Collaboration, and Social Good
Access the full transcript for this episode“I think a lot of times, we focus on data science as a tech thing, right? Oh, you're going to go work for Meta. You're going to go work for Google. You're going to go work for insert tech company here or AI startup here. And for a lot of students, especially a lot of my students, they really want to contribute to their communities and give back, right? They're thinking about how to make their community stronger. And when we only focus on the tech approach, that's very sort of up here, over there, you know, they know they'll make good money. And so they might pursue that, but they don't realize that data science can be used for a lot of good as well. You can use it in ways that actually serve the community, serve the world, from helping develop algorithms that can read MRIs or other medical imaging data, to help diagnose some sort of disease or cancer, or to identify human rights violations by being able to search massive amounts of documentation.”Today, we sit down with Judith Canner, a professor of statistics at California State University, Monterey Bay. Judith begins by reflecting on her role in redesigning first-year mathematics and statistics courses in response to some of the CSU’s executive orders, which took away traditional remedial mathematics classes. She explains to listeners how co-requisite courses and active learning strategies help students succeed, as well as touches on the importance of quantitative reasoning across a variety of disciplines. She talks about the effectiveness of pair programming within her teaching strategies, and implores people to reframe data science as a tool for social impact rather than just a way to a high-paying traditional tech job. Judith ends off by reminding fellow data science educators that data science is constantly evolving, so educators shouldn’t be afraid to embrace change and collaboration.“Don't be afraid to take a chance. The reality is that data science is still a little undefined and still constantly changing. And working in the Cal State system, I'm often confined by the system itself, right? We have to work within multiple systems when it comes to curriculum, but I'm seeing more and more educators really taking risks and more and more folks really thinking about, can we do this a completely different way than we've always done it? And so, not being afraid to take those risks. Can we teach math in a way completely different than we've always done it? Being OK with letting go of the status quo…” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com
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