The Data Science Education Podcast

Access the full transcript for this episode“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 NoblesToday, 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com

Access the full transcript for this episode“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.” This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com