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Learn data science with Posit videos
Explore Posit’s video library for R, Python, and data science tutorials. Learn best practices, workflows, and techniques from our experts.
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677 Resources
Data science hangout
Gabriela de Queiroz, Dan Boisvert, & Makarand Malu
Join us with Gabriela, Dan, and Makarand to chat about career progression, building the...
Ebook
Scaling Code-First, AI-Powered Data Science on Snowflake: The Posit Playbook for Financial Services and Insurance
Learn how banks, insurers, and capital markets firms run R and Python natively inside Snowflake, with the governance regulators require and the flexibility data science teams demand.
Data science hangout
Martin Frigaard
Join us with Martin, a Sr. Shiny Developer, to chat about application development and...
Webinar
Compliance Without Friction: Mastering the Persistent Analysis Lifecycle
Missed the live demo? You can now watch Matt Wallace, Senior Solutions Advisor at Posit, walk through how Posit’s modern platform streamlines the entire data science lifecycle. See how to go from a manager’s request to final delivery—all on your own schedule.
Ebook
From spreadsheets to strategic decisions
Learn how banks, insurers, and asset managers are replacing legacy tools with governed, open-source data science platforms to accelerate AI and reduce risk.
Data science hangout
Kyle Austin & Martin Brown
Join us with Martin and Kyle from Thermo Fisher Scientific to chat about programmatically...
Ebook
From Code to Client: The Consultant's Guide to Delivering Data Science
The complete guide to publishing, managing, and delivering your data science work with Posit Connect Cloud.
Data science hangout
Jason Frederick
Join us with Jason at Texas Capital to chat about how the data science...
Product demo
Introducing Posit AI for Positron and RStudio
You've probably tried using a general-purpose AI chatbot for data analysis at some point. And it probably worked — until it didn't. The issue isn't capability. It's context. Browser-based tools can't see your loaded packages, your dataframe schema, or the specific error you're staring at. So they generate responses that are plausible in the abstract but wrong for your actual project. For exploratory work, that's annoying. For production analysis, it's a real problem.