Interested in our platform, partnerships, etc.? Contact us today!

athlyticz.com/contact

#rstats #python #datascience #data #stats #Analytics

@veerle.hypebright.nl @davidgranjon.bsky.social @dailydatascience.bsky.social @saiemgilani.bsky.social

@athlyticz.bsky.social is backed by an autoscaled GCP framework, every student or employee can launch their own isolated environment with zero setup. From R and Python to Stan and Shiny, it all just works with multiple IDE options to choose from.

#rstats #python #statistics #datascience #rshiny

Sometimes, they start with just a thought, a problem, and the space to explore.

How do you map out your best ideas? Whiteboard? Notebook? Straight to code? Let’s discuss. 👇

But some of my favorite days?
The ones where I’m just in the thick of it—scribbling equations, sketching app architectures, and working through a problem with nothing but a marker and a board.

The best ideas don’t always start in a perfect IDE.

There’s something liberating about spilling raw ideas onto a blank canvas, seeing the chaos take form, and then bringing it all to life.

Of course, I’m with the times—I use all the tools available to streamline workflows and enhance project execution.

(1) Mastering Shiny mastering-shiny.org
(2) Engineering Production Grade Apps engineering-shiny.org
(3) Outstanding Interfaces with Shiny unleash-shiny.rinterface.com

What’s the biggest challenge you’ve faced when building data-driven apps? Let’s discuss. 👇 #rstats #rshiny #data #python

Here are three (3) free textbooks that our team followed during course development on the Athlyticz Academy platform- including one by our very own David Granjon - a MUST read

This is the exact framework we use to build next-level ML-powered Shiny apps for clients.

Make sure to check out the screenshot of our SlamStats app by Veerle, showcasing how we think through individual page designs!

📌 Phase 4: Scaling & Deployment
→ Containerize with Docker for easy deployment
→ Use Shiny Server, Posit Connect, or cloud-based hosting
→ Build in user authentication & permissions
→ Monitor app performance, latency, and errors in production

📌 Phase 3: Model Integration & Data Flow
→ Ensure models can be updated dynamically (not static CSVs)
→ Decide whether to run models locally or through APIs
→ Optimize for speed vs. accuracy (fast predictions vs. complex models)
→ Implement error handling & monitoring for model performance

📌 Phase 2: UI/UX Planning
→ Keep the interface clean, intuitive, & fast
→ Use modular UI design
→ Optimize for mobile & desktop- check out Athlyticz-funded shinyMobile by @davidgranjon.bsky.social and @veerle.hypebright.nl
→ Use progressive disclosure (show insights first, details later)

📌 Phase 1: Project Scoping & Architecture
→ Start with the business problem. What decisions will this app drive?
→ Define user personas: Who will use it, & what insights do they need?
→ Choose a tech stack: R/Shiny/Python APIs? DB? Cloud deployment?
→ Consider real-time vs batch ML model updates

✅ Machine learning models that continuously update
✅ A UI/UX that makes insights actionable
✅ A back-end that scales under heavy usage

So, how do you plan an app that works in the real world? Here’s the exact blueprint we follow at @athlyticz.bsky.social :

athlyticz.com/stan-i