Posit PBC

Do you have a community at your company that's a safe space for data people to learn and have fun together? Data communities have so many benefits! But "community building" is often no one person's job, leaving it to fall through the cracks. We want to help you navigate the process so you can have a thriving data community of practice, too, just like the Data Science Hangout ❤️ Here are some resources to help get you started. Check out this blog post by Rachael Dempsey & Libby Heeren sharing some tips they've learned over the years of building the Hangout community: https://lnkd.in/gTsKfy6Y Register for our live webinar Wednesday August 5th at 12PM ET where you'll hear from three data leaders about their experiences with internal data community building: https://pos.it/build-comm

Federal health agencies are under pressure from two directions at once: a mandate to modernize data infrastructure, and a mission-critical need to deliver accurate, timely analysis. Research pipelines built on legacy statistical systems are expensive to maintain, hard to scale, and increasingly disconnected from the cloud-native environments where agency data now lives. The result: slower research cycles, duplicated tooling costs, and data science teams stuck fighting infrastructure instead of driving outcomes. Our new paper, Data Science for Federal Health Agencies, covers how agencies are making the move to secure, open-source data science at scale: - What modern infrastructure looks like in a compliant agency environment - How teams connect R and Python to cloud data platforms like Snowflake - Where most organizations start, including teams facing open-source migration mandates like the 2027 SHARE IT Act deadline We have a long track record here, including our partnership with ICF and the CDC on the largest public health surveillance system in the country. Download the paper: https://lnkd.in/eZw5b9uX #DataScience #PublicSector #FederalHealth #rstats #Python

If you need your manager's sign-off to be at posit::conf(2026), we put together a guide to make that conversation easier. It includes a customizable email template you can send directly to your manager, with placeholders for specific sessions, budget figures, and framing around how attendance connects to your current work. A few things that tend to land well in these requests. The workshop angle works because it's specific. Seven full-day workshops run on September 14, taught by the people who built the tools. Hadley Wickham and Jenny Bryan on the modern R workflow, Garrick Aden-Buie and Sara Altman on programming with LLMs, Max Kuhn and Emil Hvitfeldt on machine learning with tidymodels (just to name a few!). A request that names a workshop and connects it to a project or skill gap on your team is harder to say no to than a generic conference attendance request. The community case matters too. 119 talks from practitioners across finance, pharma, government, academia, and tech — keynotes from Sara Altman and Simon P. Couch (Posit's AI Core team), Christine Y. Zhang (New York Times), Emily Riederer (Capital One), and Wes McKinney. These are people sharing what it actually took to make something work, not pitching products. You will be joined by data professionals from hundreds of organizations including Progressive Insurance, Pfizer, Mayo Clinic, CDC, Johnson & Johnson, Netflix, National Geographic, ExxonMobil, Dow, Sandia National Laboratories, Stanford University, Smithfield Foods, and Verizon. If you're bringing colleagues: the group rate is $1,099 per person for groups of 3 or more. Worth building into your request from the start. It shifts the conversation from "can one person go" to "should we send the team." The approval guide is linked below. Houston. September 14–16. Approval Guide (https://lnkd.in/ekqbFbyX) and check out this high-level schedule (https://lnkd.in/eKjkQhAz) to help you decide which talks you want to prioritize! posit.co/conference

🚀 Posit Package Manager is now generally available in the Posit Team Native App on Snowflake! 🚀 It scans every package against the OSV vulnerability database and keeps date-based snapshots, so your environments stay reproducible without extra setup. Posit Assistant also just landed in RStudio, plus a terminal CLI if you'd rather stay in the command line. ➡ More details on the blog! https://lnkd.in/eJd8KGEz #rstats #python #datascience #snowflake

Joe Cheng, CTO of Posit, was genuinely unsure about whether he should keep building AI tools at all. On this episode of The Test Set, he explains how he landed on an answer. His logic: The train keeps moving with or without him. If he personally stepped away from AI, or even got his whole company to abstain, it wouldn't slow the technology's development anywhere else. Joe points to Posit’s decades of perspectives about reproducible, correct data science and the leverage to help people avoid getting confidently wrong answers from these tools. He argues that's exactly why the company has an obligation to stay in the room. Listen on Spotify (https://lnkd.in/eJkWiVFR) and Apple Podcasts (https://lnkd.in/ezmaa8_G).

Hot take: if someone is making a decision based on the work, that's production. We love this reframe because it changes how we think about deploying data science. It's not one thing. There are actually two, and both matter. ⚡️ The first is high-velocity production, and it's all about speed: the internal dashboards, apps, API endpoints, and reports that help your team explore faster and make better calls. This is what Tareef Kawaf, our CEO calls "lowercase p production," where speed and iteration are the whole point. A little downtime here is no big deal, so this is where you get to move quickly. 🔒 The second is mission-critical production, for the systems that directly touch revenue: your customer, public, or regulatory-facing tools with strict SLAs, real security needs, and audited processes. The app that helps someone make a better decision this week doesn't need the same guardrails as the systems your business literally cannot afford to take offline. The teams that do this well embrace both. Once you can tell the two apart, you get to move fast and stay resilient at the same time. Best of both worlds. ☺️ Tareef breaks it down here: https://lnkd.in/g5fkWph6

How fast can you get a working AI app in front of a client? In our recent webinar, we put that to the test: taking on an unfamiliar real estate analytics project and delivering an AI-powered dashboard the client could actually use. Consulting work is varied by nature. Different clients bring different problems, different domains, and different expectations. The consultants who deliver consistently are the ones who can prototype quickly, share work easily, and iterate until it fits. What we covered: - Building a working prototype with Posit AI in RStudio and Positron, starting from client requirements - Publishing to Connect Cloud and sharing it securely with an external client the same day, no IT involvement - Access controls that scale from a single external viewer to team-wide or public sharing - Managing API keys, compute, and custom domains for a polished client handoff - Moving from a working POC to a delivered asset without rebuilding from scratch If you build and deliver data products for clients, or just want a faster path from prototype to production, the replay is worth your time. Watch on demand: https://lnkd.in/e5rK2SHv #DataScience #AI #RStats #Python

No amount of time-series forecasting is cracking this one... Semifinals are set: France vs. Spain, England vs. Argentina. We're leaving the prediction to the crowd instead of a model this time. Who's lifting the trophy? 👀 👇 Have you built any models for the World Cup? Share in the comments!

Two of the most technical session tracks at posit::conf(2026) are built for people who take R seriously as a craft. The R Nerds track covers R optimization from profiling all the way to rewriting critical functions in Rust, unit testing patterns for complex codebases, and the design principles behind good R functions. Technical, opinionated, and exactly what it sounds like. The Shiny in Pieces track goes deep on engineering Shiny apps with real architecture. Sessions cover building custom UI packages with AI, modular frameworks for clinical trial data at scale, testing strategy when code generation is cheap but attention is expensive, and how to take a single-file app.R all the way to a production package. If either of those sounds like your kind of afternoon, the full program is live now. Browse the sessions: [https://lnkd.in/eVAsNz6V] Register: [pos.it/conf] #positconf2026 #rstats #shiny #datascience