Sports data science, taught live
AthlyticZ teaches production data science through sport. Live sessions with named working practitioners, every student in a fully provisioned professional environment in one click. The sport is the hook. The skill is the point.
Every session is taught live by someone who does this work. The recording, the code, and the environment stay with you afterwards.
Scott Spencer, PhDTeaches applied Bayesian modeling
See the course →
Course
Build LLM Tools That Get Evaluated
SynergiZing ML and LLMs in R
A tidymodels pipeline, a retrieval pipeline, a deployed API and a chat interface.
RtidymodelsRAG
Nic Crane, PhDTeaches LLM-powered tooling in R
See the course →
Bundle
Build Apps a Front Office Opens Daily
The Complete Shiny Engineering Stack
Modular architecture, custom components, mobile, and a real deployment.
RShinyJavaScript
Veerle Eeftink-van LeemputTeaches production Shiny engineering
See the course →
Course
Build Machine Learning Pipelines
Python MachineZ
scikit-learn end to end, with evaluation honest enough to survive review.
Pythonscikit-learnGAMs
Patrick McFarlaneTeaches predictive modeling on sports data
See the course →
Bundle
Build Bayesian Models in Sports
Becoming a BayeZian
From first principles to the specialist models teams hire for.
StanRGaussian processes
Scott Spencer, PhDTeaches applied Bayesian modeling
See the course →
Course
Build LLM Tools That Get Evaluated
SynergiZing ML and LLMs in R
A tidymodels pipeline, a retrieval pipeline, a deployed API and a chat interface.
RtidymodelsRAG
Nic Crane, PhDTeaches LLM-powered tooling in R
See the course →
Bundle
Build Apps a Front Office Opens Daily
The Complete Shiny Engineering Stack
Modular architecture, custom components, mobile, and a real deployment.
RShinyJavaScript
Veerle Eeftink-van LeemputTeaches production Shiny engineering
See the course →
Course
Build Machine Learning Pipelines
Python MachineZ
scikit-learn end to end, with evaluation honest enough to survive review.
Pythonscikit-learnGAMs
Patrick McFarlaneTeaches predictive modeling on sports data
See the course →
Prices live on each course page, which is also where you enroll.
Run with cynkra and taught by David Granjon. You write five blockr blocks, wire them into a board, and put your own data behind it. Built live on a stage of the Tour de France Femmes.
Schematic. Ours, drawn for this page: the climbs are categorized but deliberately unnamed and the elevations are invented, because a drawing should not be mistaken for a reading. The distance, the eight categorized climbs, the ascent and the snapshot count above are the real ones.
I've always been intrigued by the intersection of sports analytics and data science. AthlyticZ provides students with the perfect platform to explore this passion. The hands-on projects are particularly empowering, allowing them to apply theoretical concepts to real-world scenarios.
AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The instructors are the best of the best, and the practical projects have immediate impact to students.Trimmed with permission.
The sports industry moves fast. Managing the pace of evolving technology, data, and AI isn't optional - it's imperative. Our Analytics team was looking for a partner to meet us where we are, to help us in the continued investment of our people and capabilities without pulling us away from the work that matters. AthlyticZ delivers exactly that: a structured, practitioner-led platform that fits how modern sports analytics groups actually operate.
We've teamed up with AthlyticZ to supercharge how we use data science and statistics to impact our sports. More clarity in the numbers means bigger results for our athletes.From a public LinkedIn post.
I've always been intrigued by the intersection of sports analytics and data science. AthlyticZ provides students with the perfect platform to explore this passion. The hands-on projects are particularly empowering, allowing them to apply theoretical concepts to real-world scenarios.
AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The instructors are the best of the best, and the practical projects have immediate impact to students.Trimmed with permission.
The sports industry moves fast. Managing the pace of evolving technology, data, and AI isn't optional - it's imperative. Our Analytics team was looking for a partner to meet us where we are, to help us in the continued investment of our people and capabilities without pulling us away from the work that matters. AthlyticZ delivers exactly that: a structured, practitioner-led platform that fits how modern sports analytics groups actually operate.
We've teamed up with AthlyticZ to supercharge how we use data science and statistics to impact our sports. More clarity in the numbers means bigger results for our athletes.From a public LinkedIn post.
The same practitioners, the same live rooms, run for one group. Organizations come to us when the methods their people need are moving faster than any recorded backlog can keep up with.
Keep a group current on methods that move quarterly, taught live by people working in them, rather than working through a course recorded two years ago.
Run a cohort where every seat opens into the same working environment, so a department is not spending week one administering installs.
Put an analytics group on the tools and methods the people teaching them use in production, with sessions built around the work they actually do.
An institutional license is a flat fee, a number of seats, and provisioned environments for every one of them. The environments run on IDEalyze, our infrastructure platform and a different product of ours. What the fee and the seat count come to is a conversation, not a page.
The order matters. Each stop exists because the one before it happened.
Taught by someone who does this work, with moderated Q and A, so you ask the question you actually have rather than the one a recording anticipated.
Pre-configured with the packages and system dependencies that session uses, at pinned versions. Nothing to install, no version drift.
Session code, notes and cheat sheets, and a recording with a 30-day replay window. What gets built in the room does not close with it.
The Career session takes what the other tracks taught and works it into portfolio and interview material.
Not a syllabus. These are the artifacts that come up in a technical conversation, in the languages the job descriptions actually name, and every one of them is taught live by someone who builds them.
Partial pooling, priors, and full posteriors in PyMC and in Stan, with the diagnostics that say when to stop trusting a fit. NumPyro where the model needs the speed.
Python and R · Modeling trackContainerized, pinned, and shipped to cloud: Docker images, CI gates, lockfiles for both languages, and a service that keeps running after the person who built it moves on.
Docker, CI/CD, GCP and AWS · Production trackDetection and tracking models over game footage, the labeling workflow behind them, and the extraction pipeline that turns frames into spatiotemporal data you can actually model.
Python, PyTorch · Sports Analytics trackLive-streaming inputs scored as they arrive, with low-latency paths and the honest question of what a number means when it is still moving.
Python and R · Production trackRetrieval pipelines with evaluation harnesses around them, so behavior is measured rather than asserted, and a result can be explained to someone non-technical without hand-waving.
Python and R · LLM Tools trackFull-stack tools people actually open: modular architecture, custom components, R talking to JavaScript over the socket, mobile and offline.
R, JavaScript · App Development trackPlanning work so it can be delegated, running delegated builds, automated browser QA, and validation gates. The loop we run our own work through, taught as a practice rather than a tool demo.
LLM Tools and ProductionEvery one of these is taught live by a working practitioner, in the language the work is actually done in.
Everything below runs on the same live rooms and the same practitioners. The difference is what you are trying to get to.
Live sessions every week across six tracks, with the recording, the code, and the environment included.
See the scheduleA week-by-week game plan: skills, deliverables, mentor sessions, and a capstone you can defend.
See the programsPut a research team, a department, or an agency on current content with dedicated live instruction.
Talk to usThe people who teach this also build it. AI orchestration, modeling, applications.
Book a delivery callEvery live room, the recordings, the code, and the environment, behind one door.
Join the MasterclassOne subscription, every live room, and everything the sessions leave behind.
