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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.”