In this conversation, Mine Çetinkaya-Rundel, data science educator at Duke University and Posit, joins Michael, Hadley, and Wes to talk about teaching data science in a time when AI can write the code for you. Mine shares her journey from actuarial science to academia, the teaching philosophy behind the “whole game” approach, and her experiments using LLMs for instant student feedback. Along the way, the group dives into the joys and risks of coding by hand, the role of open source in the classroom, and what it’s like to work across both the R and Python communities.

What’s Inside:

• How a career in actuarial science led Mine to the world of data science and teaching
• The “whole game” approach to learning and how it helps students stay motivated
• Building an LLM-powered feedback tool for low-stakes assignments
• Balancing AI assistance with the need for hands-on coding experience
• The shared DNA of R and Python scientific computing communities
• The hidden value of live coding, pair programming, and seeing the process — not just the output

In part two of our conversation with Wes McKinney, we dig into the challenges and realities of sustaining open source development. Wes shares how funding actually works (or doesn’t), why corporate buy-in is essential, and what it’s like building tools across languages, communities, and IDEs. We also talk about the Apache Software Foundation’s role in open governance and the origin of the Positron IDE.

What’s Inside:

• Why passion isn’t enough for open source to scale
• Apache Arrow’s origin story and how it was pitched
• How open governance enables trust between competitors
• The thinking behind Positron, Posit’s next-gen IDE
• Polyglot programming – Designing tools that bridge the R/Python divide
• LLMs and data UX: Why modern IDEs need to serve both humans and models
• Day-to-day coding, advising, investing, and context-switching
• Metalheads unite

Wes McKinney’s fingerprints are all over the modern data stack — from inventing Pandas to co-creating Arrow. But before all that, Wes was organizing speedrun communities and hacking together better ways to wrangle datasets in finance. In this conversation, he shares his origin story and what makes good tools good. Stay tuned for part 2, coming soon.

What’s Inside:

• How frustration with data work led Wes to build pandas (and leave a PhD)
• A nostalgic dive into the GoldenEye speedrunning scene
• Why read_csv performance is a deeply personal crusade
• Lessons from convincing friends to quit finance and go open source
• Founding startups, launching Arrow, and the Ibis origin story
• The beauty of letting contributors take the reins
• Shout-out to Philip Cloud, pandas’ resident pun master
• Why open communities win — and what it takes to build them

Before Hadley Wickham became a pillar of modern data science, he was a spreadsheet-loving teenager making databases for his dad’s job. In this episode, he reflects on the early days of his involvement with R, the birth of tidyverse, and how real-world unpredictability — like a bear in a field — shapes data science.

What’s Inside:

• Hadley’s first brush with R code … inside a Word doc
• Consulting as a grad student — and learning what people really want from stats
• How messy Excel sheets inspired the tidy data revolution
• Writing R packages as a form of self-defense (and productivity)
• The secret sauce of building the tidyverse team
• On focus, burnout, and saying “no” to GitHub pull requests
• Current obsession: using LLMs  to make data science faster, easier, and more fun
• How writing books is a form of tidying ideas, and how a Shiny textbook led to a custom bike