Emily Riederer writes Python with an R accent, and we’re all comfortable with it. In this episode, Emily reflects on her journey through R, Python, and SQL — from lessons learned in averaging default values (oops, we're not all rich!) to discovering that column selectors are way cooler than they sound. She weighs in on the delicate art of learning in public, why frustration often makes the best teacher, and how to find your niche by solving the boring problems. Oh, Oh, and the crew casually drops that she's keynoting posit::conf 2026!

Episode Notes
Emily’s had a wild ride through modeling, data engineering, machine learning, and back again, and she knows a thing or three about the evolution of SQL tooling (from nightmare multi-page scripts to the dbt renaissance). She reveals how building internal packages became her gateway to making work enjoyable. Plus: the surprising Stata origins of column selectors, the eternal struggle of naming packages across R and Python, and why watching people code teaches you more than any tutorial ever could. The conversation gets real about imposter syndrome and the magic of tacit knowledge.

What’s Inside

• Why real-world data is chaos, not truthThe path from modeling to data engineering (and back)
• What a data pipeline really is (extract, load, transform) and why organization matters
• How dbt changed the SQL game Learning by watching: Tacit knowledge and coding over the shoulder Imposter syndrome and learning in public 
• Building internal tools to escape busyworkposit::conf 2025 keynote preview

Rebecca Barter, senior data scientist at Arine and adjunct assistant professor at the University of Utah, refuses to work on things she doesn’t care about. Lucky for us, she cares about a lot, most of all impact. In this episode, Rebecca joins The Test Set to talk about learning fast, building better tools, and staying motivated and adaptable.

She shares how moving between R, Python, SQL, and dashboards reshaped how she thinks about expertise. Plus a reflection on her recent posit::conf talk, “AI: Hype, Help, or Hindrance.”

Episode Notes

Rebecca digs into what it’s really like to work with AI every day and why humans still rule, especially in exploratory data analysis. She explains how tool building can be the fastest way out of busywork and how teaching beginners sharpened her ability to communicate clearly. The conversation circles a bigger question too: As AI keeps improving, are we headed toward a future where code looks completely different … or maybe disappears altogether?

What’s Inside

• Why motivation matters even more than productivity
• Escaping busywork by building better tools
• From R to Python to dashboards: Learning fast as a survival skill
• Reality check on AI in the IDE
• Why exploratory analysis still needs human intuition
• The 80/20 of coding: Automate the boring, protect the judgment
• Teaching beginners and earning trust
• The uncertain future of code

You’ve probably used Narwhals without realizing it. It’s the compatibility layer helping apps and libraries like Plotly play nice with Pandas, Polars, Arrow, and more — while keeping computation native instead of converting everything to Pandas. In this episode, Marco Gorelli explains how his weekend experiment turned into essential ecosystem infrastructure and why data types, not APIs, are where interoperability gets tricky. Plus what it takes to build trust and community around an open-source project.


Episode Notes
Marco shares the Narwhals origin story (including the meme-powered name), the hard edge cases that live in data types and null semantics, and why he’s cautious about using AI for code generation when correctness hinges on tiny details. We also jam on proactive “GitHub surfing,” conference talks as trust-building exercises, celebrating contributors, and how early commit messages capture the genuine excitement of building something new.


What’s Inside

• Narwhals 101: You’ve probably used it (even if you didn’t know it)
• The real interoperability traps: data types, null semantics, and “looks-the-same” operations
• Why expression systems won, and how they shaped Marco’s approach — with nods to Ibis, Polars, and Pandas
• Open source as social work: proactive outreach, trust-building, and a Discord-powered community
• Extending Narwhals to new engines, starting with the Daft plugin

In this episode, we’re joined by Kelly Bodwin — candy corn defender, board game enthusiast, and Associate Professor of Statistics and Data Science at Cal Poly. We discuss her path from English and French to statistics, how she builds teaching tools and navigates AI in the classroom, and what it takes to keep a programming community weird in the best possible way.

Episode notes

Kelly is curious, collaborative, and unafraid to lean in on quirky. Kelly shares how she balances teaching three courses with master's student supervision, applied research projects spanning Polish history and beyond, and her belief that the best part of academia is the people. We also dive into the practical and philosophical challenges of staying current in a field that reinvents itself every few years.

What's inside

• Breakfast mixology
• Building Quarto extensions with JavaScript and AI
• When ChatGPT helps students learn (and when it doesn't)
• Applied stats meets history: analyzing social networks from the Polish Revolution
• Why remarkable, welcoming communities matter more than perfect code

This episode is Part 2 of our conversation with James Blair. He explains how he found his “accidental perfect fit” as a solutions engineer and how that role became a pipeline into product management. Get a peek into the AI-powered tooling he’s now building for the Posit ecosystem, and hear how he’s using Claude Code, Positron Assistant, and DataBot to generate synthetic, industry-specific demos on the fly — plus, why the real magic is keeping humans firmly in the loop. 

Episode notes

This is a story about listening deeply to users and using AI to make that listening scale. James explains what solutions engineers actually do, how that work shaped Posit’s product team, and how synthetic data plus agents are changing the way they build demos and teach data science. 

What’s inside

• What a solutions engineer really is and why the role was such a good fit for James
• How solutions engineering became a natural pathway into product management at Posit
• Multi-agent “bot posse” workflows and why context management matters
• Using AI the right way and why code literacy, critical thinking, and staying human are the real superpowers in an AI-saturated world

In Part 1 of our conversation with James Blair, we trace his delightfully non-linear path from childhood robotics dreams to journalism to R, with a few stops in between. We hear about the Shiny app that changed his career, plus a candid roundtable with Michael, Hadley, and Wes about whether a data-science master’s still pays off in the age of AI.

Episode notes

This is a story about staying hands-on and fiercely inquisitive — whether analyzing bike telemetry or in teaching data science. James shares how early experimentation with Shiny helped shape his career, and how curiosity (not credentials) still powers meaningful work in data science.

What’s inside

• A winding path from robotics to journalism to psychology to data science
• Discovering the power of applied stats
• The value (and limits) of a data-science master’s in a shifting AI landscape
• Fighting confirmation bias: good analysis resists the answer you want

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