The Data Science Education Podcast

Access the full transcript for this episode “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...

Access the full transcript for this episode “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 rese...

Access the full transcript for this episode “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...

Access the full transcript for this episode “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 e...

Access the full transcript for this episode “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 sp...

Access the full transcript for this episode “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, Prof...

Access the full transcript for this episode “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 spea...

Access the full transcript for this episode “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...

Access the full transcript for this episode “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 actual...

Access the full transcript for this episode “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...

Access the full transcript for this episode “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 ar...

Access the full transcript for this episode “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...

Access the full transcript for this episode “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...

Access the full transcript for this episode “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 real...

Access the full transcript for this episode “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 som...

Access the full transcript for this episode “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 i...

Access the full transcript for this episode “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 Scien...

Access the full transcript for this episode “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 a...

Access the full transcript for this episode “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. Entonce...

Access the full transcript for this episode “I think a lot of times, we focus on data science as a tech thing, right? Oh, you're going to go work for Meta. You're going to go work for Google. You're going to go work for insert tech company here or AI startup here. And for a lot of students, especially a lot of my students, they really want to contribute to their communities and give back, right? T...

Access the full transcript for this episode “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 j...

Access the full transcript for this episode “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 id...

Access the full transcript for this episode “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...

Access the full transcript for this episode “ 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 journe...

Access the full transcript for this episode “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...