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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.”
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Welcome to the final episode of Season 8! Like every season, we’re spending our last episode talking to three recent data science graduates about navigating post-grad life and what it means to enter the industry. We start with Rebecca Hayes, a recent graduate of the City College of San Francisco, who current works as a data analyst and emphasizes the importance of SQL, interpersonal skills, and project management. We then listen to Jacob Cavanaugh from Cal Poly San Luis Obispo, where he shares his experiences in location analytics, highlighting the impact of introductory courses and adaptability. We end with Yash Potdar, a recent UC San Diego graduate and current software engineer at Rivian, who discusses critical thinking, problem-solving, and the role of a product design elective. Talk to you all next season!
“Do something that makes you passionate. If you love dogs, or ice cream, or music, think about how you can learn something and create something in data science using that type of data.” — Rebecca Hayes (CCSF)
“Don't forget to exercise your people skills. The technical ones are important, but at the end of the day, your employers, your coworkers, they're gonna remember the people that connected with them.” — Jacob Cavanaugh (Cal Poly SLO)
“Don't pigeonhole yourself into data science. Don't second guess yourself. If you believe that you have the basic skills and can put in the effort to learn, just apply. Don't be discouraged.” — Yash Potdar (UC San Diego)
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“One of the things that I tell my students is like, you are learning how to learn as well. And being able to provide students with the guardrails, provide them with the support that they need to realize what they find interesting. I think that's one of the things I tried to do in these large classes.”
In this episode of the Data Science Education podcast, join us in our conversation with Lisa Yan, an assistant teaching professor in Electrical Engineering and Computer Science at UC Berkeley. Discover the interdisciplinary nature of data science education, the challenges of teaching large classes, and the importance of creating a supportive community for students. Lisa Yan shares her experiences in teaching gateway classes like Data 100 and Data 101, emphasizing the need to empower students and cultivate problem-solving skills. Explore the intersection of technology, society, and power as Lisa discusses her seminars on social implications of computer technology and technology, society, and power.
“I have the realization everyday that the study of data science is not just a technical one, but that it's applicable to pretty much anything and everything because we are living in the world of data. And so understanding not just as a citizen, how the data flows around us, but also understanding how we as data scientists can change the world around us with the way that we analyze data and understand data and share our findings from data. I think that's really, really important that we continue to make such a field interdisciplinary and open to many, many different students.”
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“So we need teachers, ones that are interested in learning data science and willing to go through the growing pains of being able to learn something new. And we need pathways. We need pathways to get students interested in things, to know about data science and know what it is, into high schools.”
In this episode, we engage in a conversation with Professor Solomon Russell from El Camino College, a community college in Southern California. Professor Russell shares his journey from teaching computer science to his dedicated focus on advancing data science education, discussing the challenges and successes of introducing data science at the college level. He reflects on the diversity among students entering the data science classes and the disparities in completion rates, highlighting strategies to bridge the knowledge gap and ensure student success. Delving into his ongoing work, including the development of an intermediate data science class for community college students and securing grants to expand data science education, he underscores the importance of ethics, equity, and community building in this field.
“I really think education is about liberation, right? Liberating, like different ways that you thought in the past, liberating other people, liberating your future, like the people that come after you. So to be able to make people more conscious through data science—I think that's a huge win. But ultimately, even beyond data science, I just want people to find what they're passionate about…like, I'm a dancer, so I always think that everybody has a dance.”
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“Just like how doctors take the Hippocratic Oath, I would hope data scientists would take a similar oath and have the mantra 'first do no harm.'”
In this episode, we sit down with Professor R. Uma to discuss her work in broadening participation in data science for social justice. Professor Uma shares her journey from teaching computer science at a historically black university to using data science as a tool to make STEM education more inclusive. She talks about her innovative approaches to teaching data science to students from diverse backgrounds, her recent NSF grant to develop a data analytics certificate for non-computing majors, and the importance of ethics and inclusivity in the field of data science. Join us for an insightful conversation on the future of data science education and its potential to create a more equitable and just society!
“Many of [students] come from high schools where they had no exposure to computer science courses. And the only exposure to computers that they've had is to create some PowerPoint presentation or a Word document. They come here, they take the first CS course, which for us is a C+ class, and they're like, “No, this is not what I thought computer science was about.” And so that obviously led to huge attrition and retention problems.”
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“I wish we had a manifesto, I guess, like some guiding principles that are common to everyone teaching data science across the globe, because if you start looking at different units doing data science teaching, they're always attached to their own immediate neighborhood of discipline…But there are some commonalities, of course; we're teaching the same tools, the same techniques, and there are some of those interpersonal dynamics that I mentioned, that I wish we all taught in common, because we have those things in common.”
In this episode, we delve into the world of data science education halfway across the world with Dr. Jon Cardoso-Silva from the London School of Economics. Dr. Cardoso-Silva shares his journey from industry data scientist to educator and discusses the challenges and opportunities in the field of data science education. Discover how he integrates open-source course materials, experiments with large language models in the classroom, and explores the dynamics of data science teams. Listen in for an insightful conversation on the evolving landscape of data science education and the importance of creating a global community of educators in this field!
“It's not about the best deep learning model. It's more about, do we know what we need from each other? Do we know what the client or the user wants? Or if it's a research project that uses data science, where does that research go? What is the research objective?”
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“I noticed that my students were not engaged by the typical textbook examples that I was giving them, like the heights and weights of students or the angles of ladders against walls, and they would disengage in class…So I started asking them, what is it that y'all actually want to learn about? And they told me, they want to learn about gerrymandering, and food deserts, and also online dating, sports, social media; stuff that's more serious stuff, that's less serious, but all relevant to them. So I started bringing in data about those examples. And the class transformed. We had more students take and pass the AP Stats exam that year than the previous 16 years of the school combined.”
In this episode, we’re chatting with Dashiell Young-Saver, High School Math teacher and Founder & Executive director of Skew The Script, a nonprofit organization transforming math education by providing free and relevant math curriculum materials to teachers and students. Skew the Script focuses on creating math lessons that are engaging and relatable to students by using real-world data and examples, which Dash founded while teaching AP Statistics in San Antonio, Texas. Within this conversation, Dash discusses the organization’s expansion, how it’s curriculum maintains relevance and nonpartisanship, and how he plans to continue fostering critical thinking and data literacy.
“There's so much data out there being shared, there's so many ‘studies’ that may or may not be generalizable to the topic at hand, so to be able to facilitate between these things and to know how far the data can get you and also how to question the things in front of you is, I think, more important than trying to pedal aside.”
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“It's not a matter of should we all get involved in data or data science - it's which aspect of data science am I interested in, based on my culture, my background, my beliefs, and what's important to me. And so I think that's the beginning of introducing someone to data science, is showing them how you use it every single day.”
In this episode, we’re chatting with Kari Jordan, the Executive Director of the Carpentries. Throughout the conversation, Jordan emphasizes the importance of building a shared understanding of data science terminology and creating an inclusive, collaborative atmosphere. She highlights the significance of partnerships and the potential for data scientists to make a positive impact on a broad range of issues, which students can choose from based upon their interests. Above all, Jordan emphasizes the importance of equity within the data science community, providing increased access to everyone, no matter their background or learning style.
“Data science is for everyone. It really is. It is nothing to be coveted or held to yourself. That's one reason why I believe that open access journals, open source material, open source education, is not to be a secret. You have to collaborate in order to make change happen. And in order to get a diversity of perspectives and ideas, you need other people's opinions, and you need their expertise. No one person knows at all. And so if you're an educator, I would just encourage you to create an atmosphere that's conducive to collaboration. Because those collaborative data science projects are the ones that really change the world.”
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“It doesn't take too long to look into social phenomena quantitatively to realize that we have profound inequities that are structural in societies around the world. It's not only the United States; these inequities are promoted by those who own resources, those who want to keep certain privileges, those who keep certain wealth at the expense of others. And this introduces abject situations.”
Hello and welcome back to the sixth season of the Data Science Education Podcast! In this episode, we’re chatting with Juan Gutierrez, Professor and Chair of Mathematics at the University of Texas at San Antonio. Gutierrez begins by detailing how his research on malaria utilizing mathematical biology helped him to realize the importance of looking into social phenomena quantitatively in order to bring to light the inequities in the world. He goes on to reflect upon how his introduction to programming at 10 years old in Colombia allowed him to immigrate to the US in 2001, fluent only in programming and not yet in English, emphasizing to him the value of education.
“We have to recognize that every individual comes with different strengths and deficiencies in their knowledge. So having an adaptive learning system that adjusts to those peaks and valleys might help accelerate the discovery and the acquisition of skills so that we can truly provide meaningful pathways to competence. We want to make this as easy as possible for everybody, to bring all participants in the educational experience to a level of competency that we require in society to function properly.”
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“I realized that it’s one thing to tell stories; stories are powerful on their own. But when you couple them with the quantitative information, they become an even more powerful storyteller together”
Welcome to the final episode of Season 5! In this episode, Carrie Diaz Eaton, an Associate Professor of Digital and Computational Studies at Bates College, delves into how she intertwines her passions for social justice and data science via digital narratives, duoethnography, and other mixed-method approaches. She expands upon her own work with the Rios Institute, running an open education resource sharing and community platform for STEM education, and her sabbatical time working with the Latinx community in Rhode Island, creating a general community resources database for the Providence area.
Thanks for tuning in this season, we’ll be back in the Fall with Season 6! Until then, we’d love to hear your opinions on this season, as well as what you’d like to hear next season. I’ve linked a survey here to let us know your thoughts. See you all in the Fall!
“I realized that it’s one thing to tell stories; stories are powerful on their own. But when you couple them with the quantitative information, they become an even more powerful storyteller together.”
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“We’re starting to see many more people using JupyterLite in class because it’s much easier to grow and scale…”
In this episode, we’re speaking with Jeremy Tuloup, who is a Technical Director at QuantStack and a contributor to Project Jupyter. In this conversation, he talks about Project Jupyter’s project, JupyterLite, which will allow users to run code directly from the browser and how this can make coding more accessible to users. He also shares upcoming goals the Jupyter team is working on, as well as their plans on making interactive coding more accessible to students and educators.
“With all these developments [to Jupyter] we really hope that this is going to lower the barrier of entry for accessing these type of tools and also making interactive computing, especially in the browser with JupyterLite, more accessible to more people.”
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“An emphasis that we really put on the work that the students are doing is to think about the ethical landscape in which the work is taking place…how to do that work responsibly and to do what is actually going to be meaningful to the stakeholders versus maybe what's the coolest new technique in machine learning.”
In this episode, Sarah Stone, Executive Director of the University of Washington’s eScience Institute, discusses her work with the Data Science for Social Good program, which works with undergraduate and graduate students to create and integrate community-driven projects. She also delves into the work that the eScience Institute employs at the university, spreading data expertise across departments that each department can further customize. Finally, she ends by discussing how her Ph.D. in Oceanography has informed her of the need to train existing researchers across all disciplines.
“We need to not just be training students; we need to be training the existing faculty and existing researchers, and recognizing that a lot of this training hasn't been part of the history of those fields, so meeting people where they're at in order to be able to do that.”
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In this season’s installment of “Data Science Graduates in Industry,” we’re speaking to three UC Berkeley alumni to learn more about how they’re applying what they’ve learned at UC Berkeley in industry. Samantha Chean-Udell is a Digital Product Associate at Converse, Elda Pere is working as a Senior Data Scientist at Curate, and Carlos Ortiz is working as a Data Scientist at Snap, Inc. Tune in to hear their thoughts about how their degrees prepared them for the workforce, as well as what they wish they had known before getting into the industry.
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