In part two of our conversation with Julia Silge, we discuss how work actually ships: the boundaries, the glue, and the tools that turn noise into signal. From there, we go macro and wonder what the LLM era means for humanity’s contributions, plus how licensing is evolving to protect sustainability without abandoning openness.

Episode notes

Both practical and philosophical, this conversation spans workplace energy, team connective tissue, and the big questions LLMs have us asking in a shifting data science landscape.

What’s inside

• Julia’s system for turning scattered community signals (GitHub, Stack Overflow, discourse) into product insight
• The power of “glue” work, and where to find the wins
• From Stack Overflow to LLMs: What changed when communal Q&A became model fuel — and what that means for finding answers
• Licenses in a new era: Threading the needle between MIT-style generosity and elastic-style sustainability for platformed software
• Try Positron: Where to download, read docs, and give feedback

In part one of our conversation with Julia Silge, astronomer-turned–data-science leader, we explore why data science needs a different kind of IDE. Julia takes us inside Positron, Posit’s next-generation, data-scientist-first environment, and unpacks the day-to-day realities that make data science work unlike software engineering. Along the way, we get a first-hand account of a legendary pineapple-pizza protest and how to juggle multiple projects at once.

Episode Notes:

A behind-the-scenes tour of Positron and the workflows it’s built for, plus the stories, trade-offs, and team choreography required to ship an IDE on a living substrate. We talk extension ecosystems, upstream merges, data viewers, and more. Plus, Julia shares why applied systems (and messy, real-world data) are her happy place.

What’s Inside:

• The pineapple-pizza story that unexpectedly went viral — and what “context collapse” feels like from the inside
• Why Positron is a data-science-first IDE, optimized for analysis, not general software engineering
• Iteration vs. reproducibility: the central tension in data science workflows and how tooling can honor both
• Hadley’s cold-turkey move from RStudio, muscle memory, and finding the new ergonomic groove
• How Julia measures success by smoothing the boundaries between tools and teams
• The applied, people-and-process side of data science that keeps Julia energized

For this episode, we turn the mic around. Wes McKinney takes over the interviewer’s chair to chat with his co-host, Michael Chow. Michael’s a principal software engineer at Posit, but he started out studying how people think — literally, with a PhD in cognitive psychology. Somewhere along the way, he got hooked on data science, helped build adaptive learning tools at DataCamp, and now spends his days thinking about how to make Python easier to use and more fun.

The two dig into what drives Michael’s curiosity, how a “weird obsession with tables” turned into a beloved open source project, and the future of data science/scientists.

Episode Notes:
We explore Michael’s path from studying the mind to shaping the Python data science ecosystem. From adaptive learning platforms to Great Tables, Michael shares how following unexpected curiosities can spark tools and communities that last.

What’s Inside:

• Michael’s pivot from an academic career in data science
• Behind-the-scenes messiness of building data and learning platforms
• Open source projects born out of zany, single-minded passions
• Bringing beauty to rows and columns
• Big-picture thoughts on where data science — and open source tooling — are headed

Michael, Hadley, and Wes welcome Roger Peng, professor of statistics and data science at UT Austin and co-host of Not So Standard Deviations. Together they trace Roger’s journey from early R adopter to pioneering online educator and prolific podcaster. The conversation ranges from the accidental rise of “data science” as a field, to the tension between research papers and software maintenance, to what makes for meaningful, lasting creative work.

What’s Inside:

• Roger’s first analysis project and what it taught him about authorship and data
• Roger’s advice for students testing the waters in data science
• Why software has become the unifying language of modern statistics
• The origins of “data science” as a field and a label
• Reflections on Coursera, MOOCs, and opening education to the world
• What keeps a podcast (and a career) going strong after a decade-plus

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