The Test Set by Posit

Emily Riederer, a data science manager at Capital One and cross-language tool author, talks about her journey through R, Python, and SQL. She dives into messy real-world data, the rise of dbt and better SQL tooling, and why column selectors (yes, really) change ergonomics. She also discusses learning in public, imposter syndrome, and solving boring but high-impact problems.

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 NotesRebecca 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 InsideWhy motivation matters even more than productivityEscaping busywork by building better toolsFrom R to Python to dashboards: Learning fast as a survival skillReality check on AI in the IDEWhy exploratory analysis still needs human intuitionThe 80/20 of coding: Automate the boring, protect the judgmentTeaching beginners and earning trustThe 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 NotesMarco 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 InsideNarwhals 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” operationsWhy expression systems won, and how they shaped Marco’s approach — with nods to Ibis, Polars, and PandasOpen source as social work: proactive outreach, trust-building, and a Discord-powered communityExtending 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 notesKelly 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 insideBreakfast mixologyBuilding Quarto extensions with JavaScript and AIWhen ChatGPT helps students learn (and when it doesn't)Applied stats meets history: analyzing social networks from the Polish RevolutionWhy 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 notesThis 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 insideWhat a solutions engineer really is and why the role was such a good fit for JamesHow solutions engineering became a natural pathway into product management at PositMulti-agent “bot posse” workflows and why context management mattersUsing 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 notesThis 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 insideA winding path from robotics to journalism to psychology to data scienceDiscovering the power of applied statsThe value (and limits) of a data-science master’s in a shifting AI landscapeFighting 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 notesBoth 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 insideJulia’s system for turning scattered community signals (GitHub, Stack Overflow, discourse) into product insightThe power of “glue” work, and where to find the winsFrom Stack Overflow to LLMs: What changed when communal Q&A became model fuel — and what that means for finding answersLicenses in a new era: Threading the needle between MIT-style generosity and elastic-style sustainability for platformed softwareTry 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 insideWhy Positron is a data-science-first IDE, optimized for analysis, not general software engineeringIteration vs. reproducibility: the central tension in data science workflows and how tooling can honor bothHadley’s cold-turkey move from RStudio, muscle memory, and finding the new ergonomic grooveHow Julia measures success by smoothing the boundaries between tools and teamsThe 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 scienceBehind-the-scenes messiness of building data and learning platformsOpen source projects born out of zany, single-minded passionsBringing beauty to rows and columnsBig-picture thoughts on where data science — and open source tooling — are headed

Roger Peng, a Professor of Statistics and Data Science at UT Austin and co-host of Not So Standard Deviations, discusses his unique journey in data science. He shares insights from his early projects that shaped his passion for R and the importance of hands-on experience in education. The conversation dives into the dynamics of podcasting and how to maintain meaningful content over time. Roger also emphasizes the evolving roles of programming languages like R and SQL, and the community's pivotal role in shaping the landscape of data science.