Your Guide to Quality Data Science Products

If you're a Statistical Programming Manager, you're familiar with Monday mornings that start with three urgent requests from different Biostatistics leads. All of them need analytical applications for studies with regulatory deadlines this quarter—and your team's sprint backlog is already jammed. Executives push for delivering more while cutting costs and reducing teams. Talk about being between a rock and a hard place.

You’ve requested development support for a survival analysis application aligned with your SAP, but the team’s capacity means it’ll be at least three weeks before they can start.

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Imagine your team needs evidence-based answers to clinical questions like "Which medications can be combined with ibuprofen to help treat back pain?" The traditional approach means weeks of work. We built a working conversational AI assistant that queries this data in hours.

AI-powered rapid prototyping turns vague dashboard ideas into interactive prototypes in hours, which cuts requirements iteration by up to 90% and prevents costly misalignment.

What if you could go from concept to working clinical dashboard prototype in 48 hours instead of 4-6 weeks?

Explore how researchers analyzed vast cellular data with a visualization tool that reveals patterns hidden in 700,000 cells.

Generate and automate Quarto reports with LLMs. Build once refine with your team and deploy to Posit Connect for fresh data.

Learn how to build flexible LLM apps in Shiny for Python, handle API changes, and keep your project future-proof.

A practical guide to embedding machine learning models in Shiny apps with tips on deployment, performance, and usability.

Struggling to understand Principal Component Analysis (PCA)? This guide will demystify the concepts and demonstrate practical implementation in R programming language.

Do you feel machine learning in R leaves a lot to be desired? You’re not alone. Try R tidymodels - it’ll feel right at home if you’ve used tidyverse before.