The Data Science Education Podcast: Recent Episodes
Berkeley Data Science
In this space, you will hear from a variety of distinguished Data Science educators and professionals. The individuals we’ll speak with are diverse in experience and perspective, but share the common goal of shaping the future of Data Science Education!
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“Data science shows up in a lot of places where people don’t expect, but at the end of the day, the goal is the same: using data skills and data tools to help organizations make better decisions.”— Mike Alfaro
“If it feels hard, it’s because it’s unfamiliar. The more you do it, the easier it will get, and the more fun you’re going to have.”— Annet Isa
In this episode of the UC Berkeley Data Science Education Podcast, we speak with recent data science students Mike Alfaro and Annet Isa about their different paths into the field. Mike shares how a data visualization course at Montgomery College first introduced him to the power of storytelling with data, eventually leading him to internships in marketing analytics, transportation, and environmental work. His story highlights how community college, hands-on technical skills, and networking can open doors into data science careers.
We also hear from Annet Isa, who returned to school after two decades of professional experience and found data science through her interest in patterns, prediction, and messy data. She discusses a capstone project using GIS, AI, and aerial imagery to identify solar panel installations in Montgomery County, showing how data science can support real-world environmental work. Together, their stories offer practical advice for students beginning their own journeys, from building projects to reaching out to professionals and staying patient through the learning curve.
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“Students can say, I understand what’s in this data, because I’m part of the data.” — Alana Unfried
In this episode, we speak with Alana Unfried, Professor of Statistics at Cal State Monterey Bay, about the future of statistics and data science education. Alana shares her path from classical statistics training to undergraduate teaching, educational research, and her work on MASDER, a national project focused on measuring student motivation, attitudes, and learning environments in statistics and data science classrooms.
Alana discusses why data science education needs stronger research tools, better shared data, and a clearer understanding of what students are actually experiencing in the classroom. She explains how MASDER helps faculty collect survey data, compare their classes to national trends, and contribute to a larger picture of what is working across institutions. The conversation also explores major gaps in access to data science education, especially between highly selective and more inclusive schools, and how different departments shape what students learn. Alana also reflects on the growing role of generative AI in data science education and why faculty development will be essential as the field continues to evolve.
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“Data science, to me, is all about breaking down walls—breaking down walls between disciplines, and breaking down walls between faculty and students.”
In this episode, we speak with Kagba Suaray, Professor of Mathematics and Statistics at Cal State Long Beach, about building a more community-centered vision for data science education. Kagba shares how his work connects data science to local issues in Long Beach and Compton, from public health and housing justice to educational equity, while creating opportunities for students to learn through real, meaningful data. He discusses the power of interdisciplinary collaboration, breaking down barriers that keep students from seeing themselves as “data people,” and designing programs that make data science more inclusive, applied, and community-driven. Kagba also reflects on what it takes to build partnerships, support underrepresented students, and help communities tell their own stories through data.
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“A big passion was, how do we think through care? Improvement is fundamentally like a first-order AI problem, not just how to make it easier to do clinical care of today…but how do you make new types of things possible?…If we dig really deep into what’s happening: Why? Why is it caught at this time? Why do we see it in this way? And I think latent into every one of these problems is a frontier AI problem…Through everything—trials and evidence—I think there’s the same type of dynamism we see like in general software, and this kind of pace of change / of improvement that we feel in other parts of AI. Bringing that type of pace to health is the mission of my career, and I’m excited to work on it.”
In this episode, we sit down with Adam Yala, Assistant Professor at UC Berkeley and UCSF and co-founder of Voio, to explore how AI is reshaping the future of healthcare. Adam walks through his path from research to building real-world systems, and why computational health is emerging as its own distinct field rather than just an application of AI.
We dive into what it actually takes to build in this space, from understanding clinical complexity to navigating challenges like data access and compute. Adam also shares how his experience across academia and startups has shifted his perspective on speed, innovation, and creating meaningful impact.
Finally, he offers advice for students and aspiring data scientists, emphasizing the importance of adaptability, curiosity, and focusing on the problems you want to solve in a world where technology is constantly evolving.
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“I think we had this feeling that there’s so many students that don’t make it to calculus, and that in the field of data science and the STEM field itself, we really have a gap to fill because we’re missing all of that knowledge and expertise that those students that don’t ever get through calculus would really bring to the field.”
In this episode, we speak with Mikahl Banwarth-Kuhn (MBK), Assistant Professor of Mathematics at Cal State East Bay, about reimagining the traditional calculus pathways for today’s data science students. MBK helped develop a new course sequence, Math for Data Science, designed to remove barriers that often prevent students from reaching calculus. She discusses the motivation behind the course and whether traditional pen-and-paper calculus sequences still serve data science students today. MBK advocates for a more intuitive, application-driven approach to help students more deeply understand concepts like derivatives, optimization, and differential equations.
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“When you go out and talk to other people, you realize that you become the opposite of being siloed. You really start to realize that you might have been in an echo chamber when you were talking amongst your own colleagues, and when you start to hear other people, you go, Oh, there’s more that I could understand.”
Today, we speak with Rachel Saidi, Professor in the Math, Statistics, and Data Science Department and Data Science Program Director at Montgomery College, a two-year college outside Washington, DC. Rachel shares her path from teaching math to statistics to data science, and what it’s like to scale a data science program in the community college setting, with the goal of catering to students of all ages and experiences. She tackles holistic data science education, combining curriculum, experiential learning, speaker series, and more, while also acknowledging difficulties with constraints like faculty capacity and transfer articulation with four-year universities. Finally, she reflects on how professional organizations can help educators find community and stay on top of best practices, and offers advice to educators and learners on how to tackle data science teaching and learning today.
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“I think of data as being the base of the scientific pyramid that we have. You literally can’t do science if you don’t have data—and good data. If your data is bad, then your science is going to be bad. So really, at the heart of science and research is having good data that people can find, and people can access and use.”
In this week’s episode, we speak with Tasha Marie Snow, a cryosphere researcher who works at the intersection of Earth system science, data science, cloud computing, and open science. Snow is a Co-Founder and Lead Scientist for the CryoCloud cloud-computing community and platform, and works at both NASA and the University of Maryland. She touches on how her work with NASA satellite data, such as ICESat-2 data, focuses on making large, complex datasets more accessible and usable for researchers. She also discusses her role in supporting geoscience researchers to transition their workflows to the cloud via CryoCloud within JupyterHub, as well as the educational benefits of shared computing environments.
Listen to Tasha’s talk from JupyterCon in November here, and view the interactive Antarctica map notebook Eric mentioned here!
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“The goal of the students is not to learn how JupyterHub works. The goal is to learn what’s the topic of the course. So we want to make it as easy as possible to get into an environment where they can learn what they’re actually there to learn, and not get in their way with the tools that they’re supposed to be using.”
Welcome to season 11 of the podcast! To kick off the new season, we interviewed Min Ragan-Kelley, Senior Open Infrastructure Architect at Berkeley Institute for Data Science (BIDS) and a founding member of JupyterHub. Min discusses the origin story of JupyterHub and how it evolved into the scalable platform that students and researchers alike utilize daily, reflecting on key design decisions that have shaped the platform into what it is today. He describes the importance of the platform to “get out of the way” of students in order to best aid in learning how to operate within a computing environment. Finally, Min touches on his passion for open source projects and what he hopes to come of it in relation to data science education.
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“From my own experience, you don’t need to really be the perfect data scientist to do the work. I think, especially at Berkeley, there’s a lot of pressure to know everything. That’s not necessarily the case…For a lot of the types of work that I do and in my industry, you don’t actually need to have or be the most technical person…The thing that’s actually more important, and if you want to get hired in politics or in political work is actually having domain knowledge.” —Ian Castro
In the last episode of the season, as always, we sit down with some recent Data Science graduates from UC Berkeley. Today, we talked with Ian Castro, Political Database Manager at Equis Research and former DATA 8 course staff member, who talked about how teaching and building foundational data science courses shaped his commitment to tackling issues like housing, inequality, and political representation. We also talked with Lydia Sidhom, Data Reporter at The Washington Post, who reflected on how her experiences with DATA 8 and working for the Daily Cal helped pull her towards data journalism. Together, Ian and Lydia show how recent graduates are using data to analyze and explain the world!
“I think being a journalist—especially a data journalist—requires you to be kind of like a mini expert on every story that you do. So being curious about many different fields and diving into different kinds of data is really a big plus.” —Lydia Sidhom
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“It struck me that academic integrity is a serious issue, but one whose treatment I felt was overly punitive. I don’t want us to have to act as police for our students. Students very much want to do the work, but they often are just ignorant, for whatever reason, of what academic standards at the university level are. And so I wanted to instill this kind of restorative justice framework to make moments where students do falter and they do make mistakes, I wanted to turn those into teachable moments where they could learn, and turn what is a bad situation into perhaps a positive one.” —Taiyo Inoue
Today, we speak with Sarah Senk and Taiyo Inoue, co-hosts of My Robot Teacher, which is a podcast affiliated with the California Learning Lab. Sarah and Taiyo discuss how they both bring their respective lenses of comparative literature and mathematics to examine the question and implementation of AI in education, sharing concrete classroom and academic policy uses for LLMs. They touch on academic integrity through a restorative-justice lens, the idea of AI as an opaque cultural archive, and examining higher education as a “slow disaster.” Finally, they end with valuable advice for faculty listening in, giving tips on how to approach AI.
To hear more about Sarah and Taiyo’s thoughts about all things AI and education, listen to their podcast, My Robot Teacher!
“When we talk about cultural memory, we’re thinking about things that no one individual or social group could hold in their minds. It’s the stuff that is recorded in archives, libraries, cultural practices, arts, etc., and so all of that stuff trained large language models. And so I think you can think about large language models as a kind of archive, but a pretty opaque one.”—Sarah Senk
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“I think the biggest thing I would say is just involve students in real work as early as possible. I think sometimes we have in our mind, oh, we cannot do research with students until they’re advanced in their mathematical studies, but I’ve actually found this isn’t true. I think if there’s a compelling project and students are excited about it, they are really great at learning the tools that they need to do it, and that’s something we as faculty can also help with. Students are able to make really meaningful contributions early in their careers. In terms of teaching or mentoring, I think it’s just about teaching thinking, not tools.” —Kamila Larripa
In this episode, we speak with Kamila Larripa, Associate Professor of Mathematics and Data Science Program Lead at Cal Poly Humboldt, along with her former students John Gerving and Jonathan Juarez. Kamila shares about the development of Humboldt’s new Data Science major and its "data for good” mission, as well as her California Education Learning Lab project, which builds a cross-campus community of practice, fosters data literacy, and bring climate justice modules into introductory science courses for students. Students John and Jonathan reflect on their undergraduate research experiences, highlighting how real-world data projects helped identify their interests and build collaboration skills.
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“The driving framework of how I think about equity in my classroom is from a paper by Rochelle Gutiérrez, who is a fairly predominant math educator, about equity being of these two axes: the dominant and the critical. It has four main components—access and achievement—which form the dominant axes, and identity and power, which form the critical axes. I think of these four ideas as guiding the way that I think of equity across every classroom I design.”
In this episode, we speak with Allison Theobold, Assistant Professor of Statistics at Cal Poly SLO. Allison shares her journey from economics to statistics and data science education, and explore her research on equitable pedagogy. She discusses frameworks for equity and how these inform her teaching practices, as well as how her own experiences as a learner in the age of AI help to inform her own teaching.
“For me, a lot of this work comes from me studying and reflecting on how my pedagogy impacts who might be successful in my class, and what types of students may or may not be successful. How can I broaden that more, in terms of assessment, classroom spaces, and access to resources, whether it’s through their peers, me, or outside of class. So thinking about and reflecting on ways in which the way I’m teaching might not be as favorable for some students as opposed to others.”
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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.”
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“We're all used to tracking changes in Word, so why wouldn't we want to have something like that for our code? And we're all used to Google Docs where we can collaborate in real time, so why wouldn't we want to be doing that with our code too? So both for keeping track of changes and for facilitating collaboration, anyone who I work with, I mentor them in using GitHub”
Welcome to Season 10! To kickoff our new season, we sit down with Jade Benjamin-Chung, an Assistant Professor at Stanford University in the Department of Epidemiology and Population Health, to talk about her journey into public health and becoming a leader in reproducible data science practices. Throughout the episode, we discuss the creation of her lab manual outlining best practices in data science, mentoring in low-resource settings, and promoting ethical data practices.
“If a student isn't able to be part of data collection, then I really encourage them to build a relationship with a local collaborator who knows the data really deeply. For example, I'll have a student who is really bright with coding, but has less experience working with real world data sets. I'll have them pair up with someone from, say, Bangladesh, where I do a lot of research, and they'll kind of mentor them in coding…and the person working in Bangladesh will mentor them in the data”
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“I never thought I would find that sense of community here, especially as a transfer, because I've heard so much about the stereotypes…I think a club really helped combat that” —Avani Gireesha
In our final episode of Season 9, we hear from three graduating UC Berkeley seniors, all of whom transferred from California community colleges into the Data Science major: Avani Gireesha, Hannah Brown, and Jake Pastoria. They reflect on their transitions from community college to Berkeley, discussing the clubs, research, and experiences they’ve gained in their two years here. Listen in as they offer advice for incoming transfer students on how to prepare academically, find community, and get the most out of their Berkeley experience!
“I'm still not really used to the exam rigor here and how difficult it is, but that's totally okay. I feel challenged here, and it really pushes me to get out of my comfort zone and be a better student” —Hannah Brown
“I think these classes change the way that I view education as a whole…I'll never forget opening up my first Data 8 Jupyter notebook and submitting it. Education here is really cool, and I think you should take all these classes, especially when the professors are absolute legends in Berkeley and just computer science and data science in general” —Jake Pastoria
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“There's a famous quote by a statistician, John Tukey, who's often associated with sort of introducing and promoting the concept of exploratory data analysis. And his quote is that the best thing about being a statistician is that you get to play in everyone's backyard, by which he means, as a data scientist, you get to dabble in all of these different areas…the longer you work in statistics, data science and adjacent fields, you really start to see that all these stories around data that come up in different disciplines, they're actually linked through the language of statistics and mathematics. So when I start a new domain, I will usually try to start by reasoning by analogy” —Prof. Alex Franks
In this week’s episode, we talk with Professors Mike Ludkovski and Alex Franks from UC Santa Barbara about their diverse research backgrounds—ranging from stochastic modeling to sports analytics—and how they shaped their approach to data science education. Mike and Alex discuss the value of co-teaching, designing interdisciplinary curriculum, and helping students connect theory to real-world practice. They also touch on some major initiatives aimed at expanding access to data science education, including the Southern California Consortium and the Pacific Alliance for Low-Income Inclusion.
“We found out… the awareness of data science is vastly different across campuses within just a few miles of each other… we are trying to help different places stand up data science courses, programs, and share best practices. We organize events like datathons for high school and community college students” —Prof. Mike Ludkovski
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“Lo nuevo que va a entrar al curso esta vez es la pregunta de qué hacemos con las herramientas de inteligencia artificial en este contexto. ¿Cómo? ¿Cómo usar? Yo no voy a pretender que eso no existe. Yo creo que es absurdo hoy en día imaginarnos que los estudiantes no lo van a usar. Prohibirles usar esas herramientas yo creo que es, es, es fútil. Entonces la pregunta mía es bueno, cómo le creo a los estudiantes un ambiente en el cual sepa que su privacidad está siendo respetada, que tienen acceso a herramientas que pueden usar potencialmente en su propio computador.”
In our second Spanish-speaking episode of the podcast, Eric Van Dusen and special guest host Edwin Vargas Navarro sit down with Fernando Pérez, who is the Faculty Director of the Berkeley Institute for Data Science at UC Berkeley (BIDS), a Professor of Statistics, and co-founder of Project Jupyter and IPython. Fernando reflects on his path from physics to computational science, as well as the role of open-source tools and interactive computing in the development of Juptyer Notebooks. We touch on the evolution of Jupyter and how it furthers interdisciplinary and reproducible collaboration, and discuss Fernando’s teaching philosophy through courses like STAT 159, a course that emphasized reproducibility and collaborative computing. He speaks on the challenges of AI integration in education, and offers broader advice to fellow data science educators on how to approach this quickly-evolving landscape.
En nuestro segundo episodio en español del podcast, Eric Van Dusen y el invitado especial Edwin Vargas Navarro conversan con Fernando Pérez, quien es el Director de Facultad del Berkeley Institute for Data Science (BIDS) en UC Berkeley, profesor de Estadística y cofundador de Project Jupyter e IPython. Fernando reflexiona sobre su camino desde la física hasta la ciencia computacional, así como el papel de las herramientas de código abierto y la computación interactiva en el desarrollo de los Jupyter Notebooks. Tocamos la evolución de Jupyter y cómo promueve la colaboración interdisciplinaria y reproducible, y discutimos la filosofía de enseñanza de Fernando a través de cursos como STAT 159, un curso que enfatizaba la reproducibilidad y la computación colaborativa. Él habla sobre los desafíos de la integración de IA en la educación, y ofrece consejos más amplios a los educadores de ciencia de datos sobre cómo abordar este panorama en rápida evolución.
“Porque si bien la matemática puede ser la misma, el valor de la ciencia de datos es que no es puramente probabilidad estadística o álgebra lineal. Es que esos datos vienen de algún lugar concreto, vienen de una comunidad, vienen de un grupo de personas, se reflejan, reflejan aspectos de ese contexto local y las decisiones que se van a tomar sobre esos datos van a afectar a una comunidad local.”
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“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…”
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“It was in the 1970s that David Friedman and his colleagues completely changed the way statistics is taught in the world, from going from just an emphasis on calculation, calculation, calculation, without really paying any attention to, what's the question, and what can you do with the answer?… Why does anyone care? What is the calculation that you can justifiably do, given the information at hand? And then how do you interpret the answer? That is traditional statistics teaching, and I haven't strayed one step away from it. I'm still there. It's called data science now. The tools are different. And because the tools are different, we are empowered to ask questions that we wouldn't have dared to ask before. And we can answer it in ways that we couldn't before. But I still think I am teaching traditional statistics.”
Today, we sit down with Ani Adhikari, a pioneer in building data science at UC Berkeley. She explains that traditional statistics education at Berkeley has always emphasized conceptual understanding, which she continues to aim to bring to the data science curriculum. Discussing teaching methods, she reassures statistics educators transitioning into data science that they don’t need to fundamentally change their approach—just the tools they use. Looking towards the future, Ani emphasizes AI’s rapid development, stressing the importance of equipping students with fundamental reasoning skills that will remain relevant regardless of how the industry continues to change. She ends by urging fellow educators to respect the history of data science, build on it, and remain aligned with their own intellectual and philosophical teaching goals.
“Think about why: why are you wanting to be a data science educator? That answer will be very different for many people. But trying to get to the core of that answer is the key. What is your intellectual, philosophical reason? And then make sure that everything you do, you always ask yourself: am I achieving those philosophical intellectual goals that I had? And please, please, please respect the history. Do not think of data science education as something brand new. It has been happening since people started making decisions… Know the history, respect the history, and build on it. And then you will be fulfilled, and so will your students be.”
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“I think one of the things we've approached in our data science curriculum is this idea that data science is a team sport…You're never really doing data science on your own. You're always in a team and you're working with product managers. You're working with end users. You're working with software engineers. You're working with salespeople. And that idea of how do I translate people's problems? What is my system going to do? What are the variables and what considerations I have when I'm designing a system with people? What are the algorithms going to do and what does that mean? So that kind of idea of treating it as a team sport and figuring that out as a student, is like a fundamental principle for how we do data science in these environments.”
In this episode, we sit down with Paul Groth, Professor of Algorithmic Data Science at the University of Amsterdam. Throughout the episode, Paul shares the structure of data science and AI education in the Netherlands, highlighting how the Netherlands had AI undergraduate programs before data science became mainstream. He touches on the differences between AI education and data science, as well as his thoughts on treating scholarly publishing as structured data in his role as co-scientific director of the Discovery Lab. Finally, he ends with his approach to teaching and mentorship, approaching data science as a “team sport”.
“In the Netherlands, we taught AI before we ever taught data science. So actually, we have I think one of the first few places in the world where we have a bachelor's in AI. So I think it’s different from the US system, where you might start off with a general curriculum and then specialize in something like computer science. Here, our undergraduate students come straight from high school and go directly into a subject. So we have, for example, they can go directly into AI and then they'll do a three-year bachelor's in AI.”
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“A lot of times, you think as a young woman, well, I want to do something where I can care for the world, where I can make a difference in the world to make it better. Women often don't relate that to computer programming. And so that's where data science comes in and tells a story, where it combines not just the computational skills, but the statistical skills. But then there's also a requirement of having domain expertise. And that domain expertise, that can be anything. It can be healthcare. It could be climate change. It could be whatever field you want to enter. And so it's easier to relate that to caring for people or caring or having an impact in the world.”
Today, we welcome Nathalie Guebels, Assistant Professor of Computer Science at Santa Barbara City College (SBCC), as she shares her transition from industry to academia and her larger commitment to making data science accessible to community college students. We touch on the development of SBCC's data science pathway, designed to create smooth transfer opportunities for students entering four-year universities. Nathalie also highlights her passion for supporting women in STEM, and details how she incorporates real-world datasets tailored to SBCC students. She talks about designing and co-teaching the cross-disciplinary course "Data Science for All," as well as reflects on the key role that collaboration has played in shaping SBCC’s growing data science program.
“Bringing more real world examples and your own stories into the classroom gets the students more engaged and excited with the material because they connect it with their lives and their interests…I'll look at data from my classes, or we'll talk about SBCC farmers markets. But I think one of the main things we've changed…is bringing more focus on SBCC students and on the students themselves.”
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“If you're worried that the AI hype is subsuming data science, remember: you can't have AI without data—data science isn’t going anywhere.”
Welcome to Season 9 of the show! To start off the new year, listen in on our conversation with Micaela Parker, founder and executive director of the Academic Data Science Alliance (ADSA). Micaela shares her journey into data science leadership, emphasizing the importance of inclusivity, interdisciplinarity, and community-building in the evolving fields of data science and AI. She reflects on the founding of ADSA and its mission to support faculty, staff, and students in designing, building, and sustaining data science programs. She discusses how ADSA fosters collaboration through annual meetings, working groups, and workshops that showcase innovative pedagogy and best practices, such as teaching responsible AI and integrating social justice into data science education.
“We actively solicit keynote speakers, panelists, and participants for all of our events from a diversity of backgrounds and institutions, because we believe strongly that you can only aspire to a career that you see yourself doing, and that starts with seeing someone like you in that role.”
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“I would recommend double majoring with a different degree, because I think while data science by itself is a very, very useful and versatile degree, I think being able to apply it to a particular domain overall makes you a better statistician, or economist, or historian, right?”
—Alan Liang
In the final episode of the season, we explore the pivotal role students played in shaping Berkeley’s undergraduate data science program. We sat down with three alumni — Alan Liang, Vinitra Swamy, and Gunjan Baid — who were instrumental in building the foundations of data science education at Berkeley. They reflect on their unique contributions, including developing curriculum, infrastructure, and interdisciplinary initiatives, and how those experiences shaped their career trajectories. From Alan’s insights into teaching technical concepts, to Vinitra’s innovative work on scaling Jupyter infrastructure, and Gunjan’s efforts on connector courses and technical systems, we highlight the long-lasting impact of student-led innovation.
“I really loved the experience here of being a graduate student….there's a very collaborative atmosphere that people are always super excited about working on what they're working on, and that passion is what really drew me to the PhD as well. Like, the excitement to work on ideas that might be a bit too risky, that might be a little bit out there, a bit crazy, but you know, trying to get it to work and to work alongside people that are willing to put in the late nights and early mornings, because they want to, not because someone is forcing them to.”
—Vinitra Swamy
“Really dig deep and make sure you understand the details of a problem that you're working on. This still comes up a lot for me, but if something seems like it's off, if you're training a model and something looks funky, it's probably because something is off. And I think it's easy to kind of brush over the details and kind of gloss over that. But more often than not, really kind of getting your hands dirty, peeling back the layers, looking at the data, and going deep on a problem is how you'll make the most progress.”
—Gunjan Baid
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“We introduce a new data set to them every week, and we try and use data sets that are either themed around Illinois, or themed around things that we think that they are interested in. And so that's been something that we started doing when we first piloted the course, and have continued to do that each semester. And the students really are invested in the course, because they're using real world data that they have questions about.”
—Karle Flanagan
In this episode, we explore the creation and growth of STAT 107, the University of Illinois Urbana-Champaign’s introductory data science course designed to be accessible to all students, regardless of major or background. We sat down with Karle Flanagan and Wade Fagen-Ulmschneider, the teaching professors behind STAT 107, to discuss their journey from a pilot program with 18 students to a thriving course with 1000+ students today. They detail how they built a curriculum that combines computer science and statistics, while keeping students engaged through real-world datasets, interactive live demos, and interdisciplinary collaboration. They delve into the challenges of scaling the course, the importance of co-teaching, and their broader efforts to expand data science education to high schools through initiatives like the DPI Digital Scholars Program.
“So in the very beginning, we actually started by being like, there's going to be a CS day and a Stat day, and that I would give a CS lecture, and then Karle would be there, kind of just sitting in the audience, and then Karle would give a Stat lecture the next day, and they'd be inner related, but they were kind of separated. And then one day, we were just like, I want to kind of get Karle's opinion on something and let give her perspective, because I come from an engineering background, and I am obsessed with formulas. Karle, I think, really relies more on, like, tables and graphs and like, really wants to understand the story behind data, and only once you're motivated by the story do you really want to dive in deeper. And so the way we see problems are wildly different…Students just love the fact that it's back and forth.”
—Wade Fagen-Ulmschneider
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“One of the ways we incorporate ethics is by trying to expose students to a plurality of perspectives. So we want students to hear from people with different perspectives on what it means to engage with data ethically, and so we do this by hosting guest speakers. We encourage students to take classes in a variety of departments around campus. We also try to introduce students to frameworks that can help them think about how to incorporate diverse perspectives in the creation of tech products and policy.” —Mallory Nobles
Today, we sit down with Dennis Sun and Mallory Nobles from Stanford University to discuss the university’s innovative approach to undergraduate data science education. Dennis and Mallory share insights into Stanford's dual-track offerings: the technical BS in Data Science and the interdisciplinary BA in Data Science & Social Systems. They dive into the origins and goals behind these programs, highlighting how they equip students with essential skills in data science, statistics, and ethics. The conversation also covers Stanford's emphasis on experiential learning through capstones, project-based courses, and partnerships with fields like neuroscience and engineering.
“When I came to Stanford, one challenge that was clear to me was that there were hardly any data science and machine learning classes that were accessible to freshmen or students early on in their college careers. So many of them were gated behind probability, linear algebra, and even several computer science courses. And it's a lot to ask a student to take a bunch of theoretical courses before they get to find out what data science is really about. So that was kind of the genesis of the Principles of Data Science course. It was designed to give students a sense of what data science is about, and it gives them the practical motivation to convince them that all the theoretical courses that they'll have to take are going to be worth it in the end.” —Dennis Sun
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“Entonces lo que yo procuro hacer con los estudiantes que son de áreas como de humanidades o ciencias sociales, es asociarlo como a situaciones cotidianas, haciendo analogías o buscando ejemplos de cosas que cualquiera ha experimentado. Eh como que se desarrolle esa intuición y ya después pues lo lo le ponemos como él la forma de de la sintaxis y ya el lenguaje específico que usemos”
In the podcast’s first ever Spanish speaking episode, Eric Van Dusen and special guest host Edwin Vargas Navarro sit down with Camilo Andrés De La Cruz Arboleda from the Universidad Externado de Colombia. Camilo shares his journey from studying law to embracing data science and technology, merging the two fields to innovate legal education in Colombia. He discusses how he engages law students with data science concepts, making technical subjects accessible to those without a STEM background. Camilo also explores the challenges of teaching data science in Latin America, the importance of open data, and the role of data science in sustainability and public policy.
En el primer episodio en español del podcast, Eric Van Dusen y el invitado especial Edwin Vargas Navarro conversan con Camilo Andrés De La Cruz Arboleda de la Universidad Externado en Colombia. Camilo comparte su trayectoria, desde estudiar derecho hasta abrazar la ciencia de datos y la tecnología, fusionando ambos campos para innovar la educación legal en Colombia. Habla sobre cómo involucra a los estudiantes de derecho con los conceptos de ciencia de datos, haciendo accesibles los temas técnicos para aquellos que no tienen antecedentes en STEM. Camilo también explora los desafíos de enseñar ciencia de datos en América Latina, la importancia de los datos abiertos y el papel de la ciencia de datos en la sostenibilidad y las políticas públicas.
“Yo creo que históricamente el derecho ha sido una profesión que ha estado muy reacia como a a aceptar como una revolución tecnológica y por lo menos acá en Colombia, hasta incluso hace muy pocos años se permitía hacer una audiencia por una videollamada o incluso radicar documentos por un correo electrónico es que algo que existía hace miles de de años hasta ahora, hace recientemente se se pudo incorporar dentro de del día a día de la carrera de los abogados. Si uno quiere seguir siendo competitivo, tiene cuanto menos, conocer lo que puede hacer con tecnología e incorporarlo a su a su día a día. Sea un abogado que haga eso, va a estar diez veces más preparado que el que quiera seguir como la en la en la forma tradicional, pues de llevar a cabo la profesión.”
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“I literally collected 150 jobs on Indeed.com and parsed out all of the skills that were mentioned in all the jobs, created a graphic and said, Okay, here's the courses we already have that have these skills, and here's the skills I need to create courses for.”
Today, we sat down with Crystal Wiggins, a pioneering educator in two-year college data science programs at Connecticut State Community College. Crystal shares her journey in developing Connecticut’s first two-year data science program, which has since expanded to five campuses. She discusses her innovative approach to project-based learning, teaching students to "get comfortable with the uncomfortable," and preparing them to adapt in a rapidly evolving field. Crystal also delves into her leadership role in nationwide conversations about data science in community colleges, her work with organizations like AMATYC (American Mathematical Association of Two-Year Colleges), and her vision for industry partnerships in the classroom.
“Don't be afraid to dive in. You do not need to be an expert. You can learn this with your students. There's many things that students ask me, and I'm like, Well, let me show you how to find the answer. And I was actually finding the answer for myself because I didn't know, but that's what's great about the field; it's more about teaching them how to find answers than it is knowing everything yourself. So again, my slogan, be comfortable with the uncomfortable, is like the slogan for data science for me, because you're never going to know everything, and that's what I tell my students.”
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“I developed a class called DS 100, which is in a lot of ways very similar [to Data 8], with the primary objective being, I want people to walk away from the class with saying I understand what data science is. I can do a little bit of programming, and now it's up to me whether I think it's interesting or not. I don't want anyone ever to feel like they can't do it. It's just whether or not they enjoy doing it.”
In this episode, we sit down with Langdon White from Boston University to discuss his journey from software consulting to becoming a key figure in BU's growing data science program, starting off with BU Spark! He shares the challenges of expanding a data science curriculum, including the launch of new programs, and his overarching mission to make data science accessible to students of all backgrounds. Langdon also explores innovative teaching methods like experiential learning and gamification, while highlighting the importance of diversity, ethics, and inclusivity in data science education.
“I continue to think that our biggest challenge in this industry is making sure that we have representation from all backgrounds, right?…Every student should be walking out of the school with an expectation of inclusion and diversity, but also ethics. And that the ethics falls to you…and you know, encouraging students to step up and represent themselves, from an ethical perspective, an inclusion perspective, and the diversity perspective.”
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“So what we do in Data Feminism is try to synthesize a whole lot of feminist ways of thinking about the world, that have to do with questions of bias and oppression, that have to do with questions of sort of unequal power, and who gets to make choices about how to design systems — with these sort of really broad social questions, we try to apply them to data science as both a field and as a practice.”
Join us as we engage in a conversation with Lauren F. Klein, Associate Professor at Emory University and Director of the Digital Humanities Lab. Klein shares her unique journey from a background in comparative literature to pioneering the field of digital humanities, where she bridges the gap between computational methods and humanistic inquiry. We delve into her innovative projects, including her influential "Data Feminism" book and the "Data by Design" project, exploring how these works challenge traditional data science perspectives and emphasize the importance of context, history, and ethics in data visualization.
“The point that I'm trying to make in this project is that if we take this historicized, almost literary and critical, humanistic lens to this history, we can see how the people who were designing data visualizations were either asking very similar questions to the kinds of questions about responsible data visualization that we're asking today, or they weren't. And because of that, we can see how their visualizations — far from being some sort of neutral representation of data — in fact, represented a certain policy sort of unreflective politics that I think we also need to be able to identify again, so that we don't reproduce that in the present.”
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“We're kind of in an early phase among most social scientists, trying to figure out what's new here, what's different, and how to integrate it with our standard social science methodological concerns, which I don't think we should abandon. Thinking about the relationship between theory, concept and measurement. For example, that's one of the things that social scientists bring to the table in data science projects: thinking about questions of representativeness, generalizability, and questions of causal inference.”
Welcome to the season 8 premiere! In this episode, we sit down with David J. Harding, a professor in the sociology department at UC Berkeley. David shares his unique academic journey in sociology and data science, emphasizing the integration of social science methodologies with data science tools. He discusses his work on poverty, inequality, and incarceration, and the challenges of using administrative data in research. The conversation delves into future directions for his research on adolescents and urban communities, the importance of bridging social science and data science education, and strategies for creating inclusive classroom environments.
“A standard complaint about running and estimating models in the social sciences is that we make a lot of assumptions, and then don't have the ability to test them. Then right along comes the kind of more machine learning type workflow, which allows us to learn what the model should look like from a portion of the data, and then test it and validate it on another portion of the data. I think social scientists should be building that sort of workflow into our normal work process all the time.”
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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 Raucher 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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"Now in data science, I find that what the students need is more of an ability to integrate different models and different techniques on their own."
In this episode, Vik Gopal and David Chew from the National University of Singapore (NUS) delve into the university's growing Data Science major, as well as their roles as instructors for courses in Data Visualization and Data Science in Practice. They also discuss the university-wide required data literacy course, a no-coding class taken by most first-year students, as well as share their insights into the differences between teaching Statistics and Data Science.
"We came up with projects, but the projects have to be very vague and open-ended, just like in practice, which will put [students] in a situation where they feel kind of lost...it's up to them to be resourceful to learn what they need."
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“…We try to make high-level, engaging curriculum for math, and the best part about it is we figured it out using the lens of data science.”
In this episode, Niki Wells, a K-12th Grade Curriculum Developer for Chicago Public Schools and Curriculum Editor for Skew the Script, shares her experiences teaching high school math and how she’s used cultural relevance and data science in order to get her students excited about math. While speaking on that topic, she also shares her own personal journey with math, what led her to also fall back in love with the subject, and how she uses that to create engaging content that her students love.
“For us as teachers, we really had to wake up and realize that we’re all doing the exact same thing and if we can pull from each other, we can actually make engaging math lessons that make students see that it’s real and actually want to do it.”
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“I think over time, in every discipline, there’s more and more eagerness to connect with data science, or AI, or both.”
On this episode, Yolanda Gil, a Research Professor of Computer Science and Spatial Science at the University of Southern California and Principal Scientist at USC’s Information Sciences Institute, discusses how USC makes programming and data science more accessible to non-programmers in hopes of creating more diversified data science teams. She also shares how student’s eagerness to connect with data science and AI has led USC to “rethinking” current degrees and incorporating data science across their curriculum.
“We’re not just creating new degrees, we’re also rethinking our current degrees.”
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“We take the perspective that data science is truly something unique and different, it’s not just stats plus programming…”
Hello, and welcome back to the fifth season of the Data Science Education Podcast. In this episode, we’re chatting with Bradley Voytek, a professor in cognitive science at the University of California, San Diego. He shares with us how he created the first data science class at UCSD and how that later led him to being involved in the creation of the Data Science major within UCSD’s Halıcıoğlu Data Science Institute. He also touches on his neuroscience background, how that shaped the way he approaches data science education, and why data science education should be an interdisciplinary effort between all fields.
“COGS 9 is meant to be the first foray into the world of data, for all of its goods and all of its ills; so we created that class with the intent of teaching students how to think about data and the power and the peril.”
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“You hear a lot about the metaphor ‘data is the new oil’ and I think data really is the new oil – maybe it’s even the new fabric for the way that we all interact with each other in the world…”
Hey Everyone, and welcome to the final episode of Season 4. In this episode, we’re speaking with Zarek Drozda, the director of Data Science 4 Everyone, a coalition created by the University of Chicago Center for Radical Innovation for Social Change, aimed at increasing equitable access to data science education for K-12 students. Today, he shares how the coalition is assisting high school educators in bringing data science to their schools, some of the national initiatives going on that are bringing data science to K-12, and how to get involved.
“Technology is a moving target, so I always push people towards thinking about how we can focus on the fundamentals and the fundamentals on how data is shifting society in really big ways.”
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“In this emerging world in our sciences, everyone has to understand data science and how to move forward with it.”
In this episode, we’re speaking with Deb Agarwal who is Scientific Data Division Director at Lawrence Berkeley National Laboratory, which is a United States Department of Energy national laboratory managed by the University of California. She shares her experiences getting started in data science research and how she’s seen the field of data science evolve into what we know it as today. She also speaks about why increasing representation in data science research is so important and how we can encourage diversity within the research field.
“For me, [research is about] enabling something that wasn’t possible before.”
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“...When [we] decided to take the leap and do a data science course, one of the things that we intentionally made as a requirement was for a course to have a low bar of entry, but a high probability of success.”
In today’s episode, we speak with Mahmoud Harding, who is a math instructor at North Carolina School of Science and Mathematics. At NCSSM, he teaches an introductory data science class based off of UC Berkeley’s Foundations of Data Science course. Today, he tells us about his experience bringing data science to high schoolers and how they’ve been reacting to the course. He also shares how he sees the future of data science education in high school and why it’s so important to teach high school students how to work with data.
“Data science is not just for advanced students who get accepted to a public residential high school in North Carolina. It’s for any student who’s willing to take a chance to learn something they don’t know.”
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“…But for math in general, I think data science education bridges this gap between the data that affects our lives and […] the more theoretical understandings of these concepts… ”
Today, we’re speaking with Mario Bañuelos who is an assistant professor and Associate Chair of Mathematics at California State University, Fresno. In this episode, he shares how he incorporates data science education into his math courses and how to get intellectual buy-in from students. He also speaks on the importance of linking mathematics and data science to social contexts.
“I think part of [bringing social context into math classes] is having students understand that the data affects people and that the models you create are gonna have effects on those people.”
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“The second half of the vision [for my course], is that machine learning concepts should be accessible.”
In this episode, we speak with Zachary Pardos, an associate professor at the UC Berkeley School of Education and director of the Computational Approaches to Human Learning research lab, where they focus on education data science and how to utilize AI to shape education at all levels. Today, he discusses the field of education data science and ponders its possible future. In addition, he shares some findings from the research that he’s done and from his own experiences in the classroom.
“…We need to be in conversation with policy makers, with administrators, with decision makers, so that it can be […] a joint learning process of how data scientists, domain experts, policy makers, and AI […] can collaborate.”
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For the third episode of the season, we’re bringing back three Cal Data Science alumni who are applying what they’ve learned to the healthcare industry. Each of them will be sharing their unique story and how they paved their own paths into the healthcare industry. Jordan Jomsky is currently working as a data scientist for the National Institute of Health (NIH), Tiffany Yu is pursuing her Masters Degree in Clinical Informatics Management at Stanford while working as a consultant at Kaiser Permanente, and Rosey Stone is working as a software engineer at Myriad Genetics. Tune in to hear about their undergraduate journeys and how they were able to combine their passions for data science, biology, and healthcare to pursue their dream careers.
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“It’s been really exciting to me in my new job … to start thinking about how data science can be taught across a whole university and touch every major, every discipline, every aspect of what we’re doing in our mission, in research, in teaching, and in engagement.”
On this episode, we welcome Rachel Levy, the executive director of North Carolina State University’s new Data Science Academy, who shares her excitement and future plans for the new program. She also gives us insight into how NC State built their new program around interdisciplinarity and how they’re creating a data science curriculum that is accessible and insightful for both students and faculty of all majors and levels of education.
“But we know that the big challenges of the future … are going to have to be solved in an interdisciplinary manner so we’re going to have to figure out how to do that, not only in our course work, but also in our research and how the public think about solving problems, and then about how we support engagement going forward.”
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“...As humans, we’re prone to fool ourselves… the good news is we can learn to use scientific reasoning and methods to counteract this constant tendency.”
Welcome, and welcome back to the Data Science Education Podcast! We’re thrilled to be back and equally as thrilled to have you listening with us again.
We’re kicking off Season 4 by speaking with Dr. Julia Koschinsky, the executive director of the Center for Spatial Data Science at the University of Chicago. In this episode, she discusses what spatial data science is and how UChicago is incorporating it into their curriculum. She also delves deep into the ideas of scientific reasoning and attitude and how UChicago uses them to re-engage their students in the “thrill of discovery.”
“What makes this exciting to me is that’s about changing the way we think about teaching data science, including spatial data science.”
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“Any paper that I write, there is some sort of data science… and I noticed how even a little bit of coding knowledge goes a long way.”
In this episode, David Broockman, an associate professor of political science at UC Berkeley, shares his perspective on how the fields of data science and political science intersect within coursework. David shares his experiences with teaching PoliSci3, a course that introduces statistics and data science to political science students. He also discusses his team-based approach to teaching the course and expresses the importance of pushing students out of their comfort zone.
“I think there is no way to be a good data science educator unless you push students outside of their comfort zone. In fact, it’s especially important to take the students who start off being a little afraid of data science and coding, because once pushed, they realize they have these capabilities they didn’t know they had!”
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In this episode, John DeNero, the Associate Dean of Undergraduate Studies in Computing, Data Science, and Society and Associate Teaching Professor in the UC Berkeley EECS department, discusses his role in bringing Data Science Education to undergraduates at Cal. He also speaks on his passion for teaching introductory courses, and how he approaches creating them. John has played an integral role throughout our UC Berkeley Data Science journey, and we hope you enjoy hearing his unique perspective and words of wisdom!
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In this special episode of the Data Science Education Podcast, we’ve featured three interviews from recent UC Berkeley Data Science graduates! All three of these graduates are currently applying their Data Science education in industry, and each has a unique perspective and story to share. Amal Bhatnagar (at the time of this interview) was working as a Data Scientist at the startup, ProducePay. Alex Nakagawa is currently working as a Full Stack Developer for the LA Clippers. Claire Dominique Medina is currently working as a Junior Data Science & Analytics Associate at Publicis Sapient. Tune in to hear these recent grads discuss how their education prepared them for their daily work, and also learn what they wish their coursework emphasized more!
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In this episode, Pamela Burdman, an independent policy analyst on college access, readiness, and the founder of Just Equations joins us to discuss how mathematics curriculum could successfully integrate within Data Science Education. She also shares her unique perspective on how Data Science education is affecting certain education policy.
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In this episode, Hunter Glanz, an associate professor at Cal Poly San Luis Obispo joins us to discuss the “fun challenge” of creating a cross-disciplinary major, such as Data Science, and how cross disciplinary collaboration and community can grow from this challenge. He also shares why it’s so important for educators to continue to be open to learning and shares a few of his favorite places to find inspiration and remain curious about Data Science research and education.
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In this episode, Kathi Fisler, a research professor and the associate director of the undergraduate Computer Science program at Brown University, speaks on how Bootstrap, a program she co-directs, introduces Data Science education to the K-12 school system. Among other things, she speaks on the importance of integrating Data Science curricula and Data Engineering into Computer Science coursework and ponders the future of Data Science education and how to foster a sense of community within the field.
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In this episode, Brian Wright, an Assistant Professor and the Director of Undergraduate Programs at the School of Data Science for the University of Virginia, discusses the importance of fostering a sense of ‘Data Intuition’ within their Data Science program. He also outlines how to build a meaningful Data Science capstone project for students, while urging educators to prioritize maintaining domain knowledge throughout Data Science coursework.
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In this episode, Sara Stoudt, an Associate Professor at Bucknell University and co-author of Communicating with Data, discusses the importance of communication in Data Science as an art and a creative medium. She sheds light on how challenging it was for her to describe her process for writing in data analysis, especially as she worked with Berkeley Professor, Deborah Nolan, on publishing their book. Stoudt also elaborates on her perspective towards the Data Science Education community - she marvels at how supportive and open educators are when it comes to the sharing of resources and shares her hopes to engage with students at deeper levels.
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In this episode, Steven Levitt, the William B. Ogden Distinguished Service Professor of Economics at the University of Chicago and co-author of Freakonomics shares his experiences as a leader in the ‘Data Science for All’ movement and as an active proponent of Data Science Education in general. Additionally, Levitt elaborates on his perspective on how Data Science integrates within the field of Economics; he discusses how courses that he teaches utilize Data Science tools in practical ways, and pinpoints the challenges in integrating these tools at different educational levels. In this conversation, Professor Levitt shares many more meaningful insights into his busy world of teaching, writing, and podcasting - we hope you enjoy this 'must-listen’ episode!
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In this episode, Gloria Washington, an assistant professor at Howard University, shares her experiences structuring her Data Science-driven course to accommodate students who may be hesitant to join the field; through this discussion, she also urges institutions to construct their Data Science curriculum with marginalized communities in mind. Later in the conversation, Gloria speaks on her research that utilizes human-centered data.
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In this episode, Suraj Rampure, a lecturer at the Halıcıoğlu Data Science Institute at UCSD and UC Berkeley graduate, shares his experience with teaching in Data Science. Reflecting on both, he speaks about the importance of student teaching assistants in developing content based on the student perspective and experience. Suraj also discusses enhancing the course offerings at UC San Diego in order to cope with the shift to virtual learning and to attract more students.
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In this episode of the Data Science Education Podcast, Katia Fuchs discusses her experiences as the Chair of City College of San Francisco’s Mathematics department. Katia also elaborates on her experiences bringing Data Science curriculum to the community college level. Katia discusses how her institution adapts to curriculum demands with varying resource levels, and calls for her community to band together!
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In this podcast episode, Rebecca Nugent, the Associate Head and Co-Director of Undergraduate Studies for the Carnegie Mellon Statistics & Data Science Department, discusses the importance of making Data Science education accessible. She speaks about her work at CMU and how she is studying how to teach Data Science and build entry points into the field for people of all backgrounds and ages in a bid to make Data Science more inclusive. She also encourages open-mindedness in teaching Data Science, highlighting that the education does not need to start with programming and people can benefit from learning about the conceptual ideas first as well. Furthermore, she emphasizes the importance of data literacy as well as the risk of inequity in society that limited access to data poses. Finally, she discusses how scaling poses a challenge in the ever-growing field of Data Science and how educators need more investment and resources for pedagogical innovations.
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Welcome back to Season 2 of The Data Science Education Podcast! We’re happy to be back, and hope you look forward to the return of biweekly episode releases here, and on our Spotify! In the seventh episode of our Podcast, Jim Colliander speaks on his experiences helping create projects like Syzygy, Callysto, and 2i2c. Through this conversation, he provides valuable perspective on how to respond to the university demand for interactive computing while maintaining and promoting STEM diversity/inclusion.
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Season 2 of The Data Science Education Podcast will begin in August! Thank you all so much for tuning in for Season 1. We hope you’ve enjoyed and are looking forward to releasing great new content in the Fall.
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In the sixth episode of the Data Science Education Podcast, Nicholas Horton discusses his experiences as a Professor at Amherst College, where an introductory Data Science course builds from existing Statistics and CS courses. Professor Horton also elaborates on the purpose of his new textbook and important considerations when driving Data Science Education forward.
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In the fifth episode of the Data Science Education Podcast, Debbie Yuster discusses her experiences adapting to the recent surge in interest towards Data Science Education. Dr. Yuster also elaborates on her unique perspective on teaching Data Ethics courses and provides valuable insight into effective ways to streamline Data Science programs at different institutions.
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In the fourth episode of the Data Science Education Podcast, Rajesh Gupta discusses his role in instituting and maintaining a Data Science Education institution at UC San Diego. Dr. Gupta also elaborates on his unique perspective on scaling curriculum to large student populations and highlights his efforts to adapt to Data Science students’ needs. Listen in as we discuss the importance of developing an inclusive and manageable Data Science program at an institution.
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In the third episode of the Data Science Education Podcast, Debbie Yuster discusses her experiences adapting to the recent surge in interest towards Data Science Education. Dr. Yuster also elaborates on her unique perspective on teaching Data Ethics courses and provides valuable insight into effective ways to streamline Data Science programs at different institutions.
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In the second episode of the Data Science Education Podcast, Chris Holdgraf discusses his experiences working with 2i2c and Project Jupyter. Dr. Holdgraf also elaborates on his unique perspective on the future of Data Science education and provides valuable insight into how to utilize proper infrastructure in a Data Science Program.
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In the first episode of the Data Science Education Podcast, Byron Chu discusses his experiences working with Cybera and Callysto. Dr. Chu elaborates on his unique perspective on the future of Data Science education and provides valuable insight into bringing Data Science to the 5-12th grade levels.Tune in to hear a rich discussion about how we could be driving Data Science education towards younger audiences.
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