The Test Set by Posit

• James Blair: Part 1 — Portfolios, practice, and staying curious

  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

  --------  

  29:31

  --------

  29:31
• Julia Silge: Part 2 — Glue work, licensing, and open source in the age of LLMs

  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

  --------  

  28:17

  --------

  28:17
• Julia Silge: Part 1 — Positron, pineapple pizza, and the art of iteration

  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

  --------  

  38:45

  --------

  38:45
• Michael Chow: From psychology and Python to constrained creativity

  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

  --------  

  1:07:24

  --------

  1:07:24
• Roger Peng: Sustaining data science — in classrooms, code, and conversations

  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 dataRoger’s advice for students testing the waters in data scienceWhy software has become the unifying language of modern statisticsThe origins of “data science” as a field and a labelReflections on Coursera, MOOCs, and opening education to the worldWhat keeps a podcast (and a career) going strong after a decade-plus

  --------  

  45:05

  --------

  45:05

More Technology podcasts

Trending Technology podcasts

About The Test Set by Posit

The Test Set by Posit: Podcasts in Family