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“You can be hoodwinked with data in the same way that you can be hoodwinked by a car salesman. And so the idea of [Calling B******t] was to step away from all the details of the black box: that's the statistical procedures, the algorithms, etc. (Not to say that we don't pay attention to what we do.) But the idea is to really pay attention to the input data that's coming in—to think about things like selection bias—to think about where that data is coming from.”

Join us in our Season 7 finale as we host Jevin West, an associate professor at the University of Washington and a co-founder of the Center for an Informed Public. Dive into a deep discussion about the intersection of data science and misinformation, the challenges of big data, and the ethical considerations that come with it. Jevin shares his experiences from the early days of data science programs, his insights on combating misinformation through education, and the evolution of his course and book, "Calling B******t." Whether you're a data science professional or a student, listen in to explore how data science education can empower us to make informed decisions and foster a more truthful society.

“One of the most important skills that we're going to want to enhance more and more is humaneness…things like being able to ask questions, to sort of work through logic to really tease out things, like correlation versus causation. Machines don't tend to do so well [with those things]—they don't have access to the physical world. That's one of their weaknesses. So you want to lean into your strategic advantages as humans…maintain that humaneness by doing things that machines can't do.”

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Join us as we speak with three different guests, all UC Berkeley Data Science alumni, who have gone on to pursue higher education. Ranging from learning sciences to epidemiology, our guests share their experiences, challenges, and insights into how their data science education prepared them for their current paths.

Ashley Quiterio, a PhD student in Learning Sciences at Northwestern University, delves into the intersection of data science and education, highlighting the transformative potential of data-driven approaches in shaping learning environments.

“Try everything and try different things. I mentioned all these different roles [I did during undergrad], where I was trying to see where I fit, deciding what I like about data education. There's all these different lenses and different ways of thinking about where you fit. So I'd encourage people to try that out, early and often. Data science is such an interdisciplinary field that you're not going to be lacking opportunities.” — Ashley Quiterio

Anna Nguyen, a PhD student in Epidemiology and Clinical Research at Stanford University, shares her journey from data science to public health, emphasizing the importance of interdisciplinary collaboration in addressing complex health challenges.

“Regardless of what anyone says, there's no pure cut way of getting into grad school. Pursuing opportunities that allow you to really explore your interests and displaying a willingness to learn is probably the best way to prepare for a masters or a PhD program. I think I definitely overestimated how much time I had in undergrad. And the time was so limited and valuable, so it's really not worth doing things that you don't enjoy in that limited time.” — Anna Nguyen

Rodrigo Palmaka, a Masters student in Statistics at UC Berkeley, offers perspectives on computational pathology and statistical research, illustrating the versatility of data science skills in diverse research domains.

“I think I always sought to focus on the fundamentals—not overfit or pigeonhole myself too much—and give myself some flexibility to, you know, be able to adapt to the next big thing.” — Rodrigo Palmaka

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“UC Merced opened in 2005, so we were starting from a very different place than lots of campuses are. So I try very hard to be really intentional about when we think about hiring people; we want to be very aware of ways that unconscious bias plays out in in hiring. When we invite people to give seminars, we try to invite people from variety of backgrounds and campuses. And so I think that being at UC Merced—a new campus with a really strong emphasis on diversity—it's very much something that’s important to the students.”

Join us in conversation with Suzanne Sindi, Professor of Applied Mathematics and Chair of the Department at UC Merced, as she shares her journey in incorporating data science concepts into her teaching, highlighting the importance of engaging students through real-world applications and interdisciplinary approaches. Suzanne discusses her involvement in diversity initiatives, such as the SIAM Activity Group in Equity, Diversity, and Inclusion, and how it shapes her teaching philosophy and fosters a more inclusive learning environment. We also touch on the challenges and opportunities of data science education in diverse settings, such as UC Merced's Central Valley location, and learn about strategies for preparing students to navigate the evolving landscape of mathematical and computational disciplines.

“So something like the mean or average value, are words that, you know, have meanings outside of math. And so now you're trying to use this in a context, like in sort of a scientific context. And one of the things I hadn't appreciated is, if you're working with people who potentially don't come from homes where they speak English at home, they don't have maybe the same context for some of those words in those terms.”

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“We are definitely a Hispanic enrolling institution, but the TIPS project is aiming to embrace that ‘serving’ term, and just the ideal of serving our Hispanic students. Through the TIPS project, there's a ton of professional development — very deep, profound professional development. We want an entire department to participate in the TIPS pathway because the department is a unit of change, meaning that the entire community and culture of that department will change, rather than just having a few people who are interested in DEI initiatives.”

Join us in discussion with Dr. Omayra Ortega, a professor at Sonoma State University, as we delve into the evolving landscape of data science education. From her journey as a mathematician with a background in music to her current endeavors in mathematical epidemiology and data science, Dr. Ortega shares insights into the intersectionality between gender, ethnicity, and inclusion in the data science community. As a former president of the National Association of Mathematicians and a passionate advocate for underrepresented groups in STEM, Dr. Ortega discusses the importance of fostering diversity and equity in data science education.

“If you're a data science educator, make friends with other data science educators because I'm sure they need help. They need your ideas, your models for how you run your degree program, for how you run your classes, and best practices. Go to those lovely workshops that are organized at UC Berkeley every summer and spring — if you're in California, join CADSE.”

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“Whenever I'm trying to teach people, I try to demystify the verbiage around computer science and data science, getting people to understand that we can talk about things in a way that makes more sense to you, by using words that you're more familiar with. When we're using all these words that people aren't familiar with, that's automatically going to get people to like retreat into a shell…we have to demystify the way that we talk about technology for people to feel like it's something that can actually be understood.”

In today’s episode, we sit down with Henry Bowe, the Lead Technical Instructor at Hack the Hood, an organization providing free tech education programs focused on exploring foundational technical skills through a justice lens. From Henry's personal journey into software engineering to the impactful work of Hack the Hood in empowering marginalized communities, listeners will gain insights into the intersection of technology, education, and social justice. Explore Hack the Hood's innovative programs, the incorporation of social justice into data science curriculum, and the importance of making technical concepts accessible.

“And we really believe that if you can give somebody the tools to really feel like they belong in that space, to really feel like they can be comfortable in that space and they can thrive in that space, then the sky is really the limit.”

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“Getting Python workshops, data analysis workshops…and our own Datathon, provided a lot of low stakes, low commitment opportunities [for students], and just getting in the faces of students, telling them they should try it out, has been helpful in at least generating excitement around data science for students to actually inquire about it.”

In this episode, join our conversation with Denise Hum, Mathematics Engineering Science Achievement (MESA) professor from Skyline College. Delve into the journey of bringing data science education to the community college level, where Denise shares her motivations, challenges, and innovative approaches. From redefining math curriculum to fostering partnerships with four-year institutions, discover how Denise is paving the way for broader access to data science education. Gain insights into the evolving landscape of STEM education and the pivotal role data science plays in shaping the future.

“I know that this is an interesting time to be in math education, with AB 1705, and the changes that that will bring. But I think that data science gives us the opportunity to really rethink math curriculum and really invigorate it. I know that data science is sort of interdisciplinary between math and computer science…I think that it invites the conversation about how we can innovate, and really an opportunity to create new courses. Yes, we will lose some courses as a result of this legislation, but at the same time, let's create some new ones.”

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“For a long time, I didn't want to write a book about statistics…But I felt that the two things that I could add, based on my BBC experience, was, one, a kind of psychological realism: a recognition that a lot of what we think is not really about, oh, you got confused between correlation and causation or something like that. The problem is you believe something because you wanted to believe it. The second thing that I wanted to introduce was just the idea that statistics can be a really positive thing, your data can be a positive thing…Even among people who are advocates for data science, it's very easy to fall into the trap of only talking about things going wrong, only talking about misinformation…I wanted to push back against that.”

In this compelling episode, we engage in a dynamic conversation with Tim Harford, renowned economist, author of “The Data Detective,” and host of BBC’s “More or Less” podcast. Harford shares his journey from economist to BBC presenter, unveiling the inspiration behind "The Data Detective" and his distinctive approach to the subject. Delving into the challenges of building trust in statistics amid contemporary skepticism, Harford underscores the importance of trustworthy data connected to real-world issues. The conversation extends to the role of educators in promoting data literacy for society, with Harford advocating for the integration of statistical thinking across academic disciplines to highlight the positive impact of data.

“So to educate us, I would say, are you teaching the three C's? Are you encouraging your students to be calm? Are you educating them in the importance of context, as well as all the technical stuff? Like all the things around the technical stuff that make all the difference? And above all, are you fostering a sense of curiosity in your students? I'm sure most educators hope to do that. But it's always a good idea to remind ourselves.”

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“Our focus is very balanced across foundations and applications, we feel that they're hand in hand. But the Northstar of what we're building is a new discipline...We understand that Data Science is going to not just take a bunch of disciplines together to form a new discipline, but it's actually going to take things that are not even at the university.”

Hello and welcome back to the seventh season of the Data Science Education Podcast! In this episode, we’re chatting with David Uminsky, Executive Director of the Data Science Institute at the University of Chicago. We begin by exploring Uminsky’s career evolution from a mathematician to a key player in the Data Science education sphere, and then shift to insights into the innovative initiatives happening at the University of Chicago, including the development of intentional doctoral programs and the groundbreaking preceptorship program that bridges the gap between academia and community colleges.

“When we're having these conversations with the community colleges, I was thinking: Wait a minute, there's a real thing here that they want. And what they wanted was to make sure that there were 100,000+ students being served by these incredible seven campuses plus, that were at risk of being left out of the data and AI revolution, the workforce training, and the educational pathways. And they wanted to form a partnership around that with UChicago.”