Michael S. Czahor, PhD

Only 1 Week away!

We’re excited to be exhibiting at JSM in Nashville from Aug 2–7 and we’d love to see you there!

Come find us at Booth #405 stop by to say hi, meet the team, and hear what we’ve been building.

Let’s keep learning, building, and making an impact together.
See you soon at #JSM2025

@athlyticz.bsky.social is dropping a full Free Shiny training on reactivity tomorrow. Join and build a sports-based app on our platform.

Want in? Simply comment "interested" on this post and we will respond once its ready for you to register.

#RSTATS #rshiny #sports #sportsanalytics

@athlyticz.bsky.social is dropping a full Free Shiny training on reactivity tomorrow. Join and build a sports-based app on our platform.

Want in? Simply comment "interested" on this post and we will respond once its ready for you to register.

#RSTATS #rshiny #sports #sportsanalytics

Contact Athlyticz — Tech Support, Partnerships & Media Inquiries Reach out to Athlyticz for tech support, business licensing, or group training. Connect with us via email or social media. We're here to help. Let me know if you want a version more focused on studen...

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

Us too Kane, us too

#rstats #datascience #data #Analytics #python

@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

A quick tour of what it looks like to launch apps/run code inside Athlyticz

Our custom-built platform was engineered for scale from day 1 — enabling teams across businesses & universities to run high-end models & deploy production-grade apps.

Check out this app course by @veerle.hypebright.nl

@athlyticz.bsky.social Youtube is officially being managed by our marketing team - you can expect consistent, high quality tutorials for data science using sports data. Make sure to subscribe at

www.youtube.com/@AthlyticZ

#rstats #python #sports #datascience #dataanalytics #data #machinelearning

Learn Bayesian Modeling Through Sports Explore how data science meets the court and pitch. In this free preview of Becoming a BayeZian I, build your first Bayesian models using real NBA and soccer data. Perfect for analysts, students, and ...

For anyone who wants to learn Stan - we’ve made a full module free on AthlyticZ Academy to learn from Dr. Scott Spencer.

Access 8 Stan Model files for soccer & basketball.

This is Module 15 in course 1 of our Becoming A BayeZian Series - enjoy !

#rstats

athlyticz.com/stan-i-preview

If your team views Linear Regression as a mysterious ancient language, it might be time for a training session.

AthlyticZ offers training in Python, R, Bayesian, + more. Get in touch for AthlyticZ trainings for yourself or your team.

#DataScience #AthlyticZ

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.

Believe it or not, I still write out all of my models by hand.

I’ve had whiteboards in every place I’ve ever lived—mapping out everything from stakeholder needs and app designs to complex statistical models.

(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 :

🚀 Building a State-of-the-Art ML-Powered Shiny App: A Step-by-Step Guide 🚀

Most Shiny apps fail not because of bad code, but because they aren’t designed for real-world use.

A truly scalable, production-grade Shiny app needs to integrate ... a thread 🧵

Are you new to coding? AthlyticZ offers beginner-friendly training in Python and R programming:

1. FoundationZ of Data Science: Intro to Python for Data Science (athlyticz.com/fds)

2. BreeZing Through the Tidyverse: Intro to R for Data Science (athlyticz.com/tidy-i)

#DataScience

Embrace Bayesian Analysis with Sports Insights Embark on a Bayesian journey with our Becoming a BayeZian course, where we dive into sports analytics to unlock the power of Bayesian analysis.

athlyticz.com/stan-i

Advanced Bayesian Modeling in Stan Advance your Bayesian journey with Becoming a BayeZian II! Dive deeper and master advanced Bayesian techniques with Stan. Explore cutting-edge models and tools to elevate your data analysis to the nex...

athlyticz.com/stan-ii

Autoregressive techniques, survival analysis, differential and difference equations, splines, Gaussian processes, Hilbert-space approximate gaussian processes, physics-constrained models, etc.

You can check out our flagship Bayesian trainings which you'll be working alongside in the links below ⬇️

These are paid, contract positions at a very competitive rate.

Candidates must have demonstrated experience in as many of these topics as possible using Stan: mixture models, rating and ranking models, advanced multilevel models, QR reparameterization, .......

To apply please send your resume and any relevant work samples to admin@athlyticz.com and someone from our team will review asap. Please do not send cover letters. Referrals are helpful if anyone has someone really good - please contact the same email for referrals.

AthlyticZ is looking for 1-2 content creators/instructors to create comprehensive monthly case studies, complimenting two of our trainings -- both for applied bayesian modeling in Stan. #rstats #data #datascience