Courses
Learn the thing, then go and do it.
Self-paced courses built by working practitioners, each one leading to something you can build rather than something you have watched. Every course here also sits inside a Career Program, so it can be a single skill or one leg of a route.
Pick what you want to be able to build.
Taught byScott Spencer, PhDTeaches applied Bayesian modeling
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Course
Build Machine Learning Pipelines
Python MachineZ
scikit-learn end to end, with evaluation honest enough to survive review.
Taught byPatrick McFarlaneTeaches predictive modeling on sports data
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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.
Taught byNic Crane, PhDTeaches LLM-powered tooling in R
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Bundle
Build Apps a Front Office Opens Daily
The Complete Shiny Engineering Stack
Modular architecture, custom components, mobile, and a real deployment.
Taught byVeerle Eeftink-van LeemputTeaches production Shiny engineering
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Course
Build Bayesian Models in Stan
Becoming a BayeZian I
Priors, posteriors, GLMs and hierarchical models, coded from scratch.
Taught byScott Spencer, PhDTeaches applied Bayesian modeling
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Course
Build Advanced Bayesian Models
Becoming a BayeZian II
Survival, Gaussian processes and physics-constrained systems in production Stan.
Taught byScott Spencer, PhDTeaches advanced Bayesian methods
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Course
Ship Shiny Apps to Production
ProductioniZing Shiny Applications
Modules, tests, deployment and scaling, on somebody else's machine.
Taught byVeerle Eeftink-van LeemputTeaches production Shiny engineering
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Course
Build Custom Shiny Components
Outstanding UIs: CustomiZing WidgetZ
Your own inputs, outputs and htmlwidgets, with R talking to JavaScript.
Taught byVeerle Eeftink-van LeemputTeaches custom Shiny component development
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Course
Put Your Analysis on a Phone
Outstanding UIs: Mobile StructureZ
Installable mobile Shiny apps and progressive web apps, offline included.
Taught byVeerle Eeftink-van LeemputTeaches mobile and offline Shiny
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Course
Write Python That Answers Questions
FoundationZ of Data Science
Load it, clean it, analyze it, and produce a result someone can reproduce.
Taught byEvan CallaghanTeaches Python and analysis from the beginning
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Course
Build Reports That Rerun Themselves
BreeZing Through the Tidyverse
Wrangle and visualize fluently in R, and publish something reproducible.
Taught byPaul Sabin, PhDTeaches applied analytics in R
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Bundle
Go From First Script to Shipped Model
Lifetime Python Bundle
The whole Python route: foundations, then a production machine learning pipeline.
Taught byPatrick McFarlaneTeaches predictive modeling on sports data
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Two doors, and they are not the same question.
A single course answers a skill you are missing. A path answers the work you are trying to move into. Both start here.
Career Programs
A week-by-week game plan with the courses inside it, plus mentor sessions, simulated interviews, and a capstone. Take this if you want the route rather than the parts.
See the programsMasterclass
Live sessions every week with the same practitioners, in the same provisioned environment. Take this if you want to see how it is taught before committing to a route.
See this week’s sessionsGrouped by what you want to be able to do.
Every entry leads with the capability rather than the syllabus, names the tools honestly, and says which Career Program it feeds.
Start here
The two foundations everything else sits on, one in each working language. If you are not sure which, take the one in the language your target work is written in.
FoundationZ of Data Science
Write and run Python on real data: load it, clean it, analyze it, and produce a result somebody else can reproduce.
Taught by Evan Callaghan. Teaches Python and analysis from the beginning.
Feeds into Sports Quantitative Analyst, Production Data Scientist, Junior Sports Data Scientist
BreeZing Through the Tidyverse
Wrangle and visualize data fluently in R, and publish a report that reruns and gives the same answer.
Taught by Paul Sabin, PhD. Teaches applied analytics in R.
Feeds into Shiny Sports Engineer, Junior Sports Data Scientist
Modeling and machine learning
Uncertainty done properly and pipelines that survive review. This is the stack quantitative and machine learning roles screen for hardest.
Becoming a BayeZian I
Code Bayesian models in Stan from priors through posteriors, and build the GLMs and hierarchical models that come up in every quantitative interview.
Taught by Scott Spencer, PhD. Teaches applied Bayesian modeling.
Feeds into Sports Quantitative Analyst
Becoming a BayeZian II
Fit the models teams hire specialists for: mixture, survival, and ordinal models, Gaussian processes, and physics-constrained and differential-equation models.
Taught by Scott Spencer, PhD. Teaches advanced Bayesian methods.
Feeds into Sports Quantitative Analyst
Python MachineZ
Build a machine learning pipeline in scikit-learn and evaluate it honestly, including knowing when to reach for a GAM, a tree, or a network.
Taught by Patrick McFarlane. Teaches predictive modeling on sports data.
Feeds into Production Data Scientist
Application development
Turning analysis into something people open. Modular architecture, custom components, mobile, and deployment.
ProductioniZing Shiny Applications
Build and deploy production Shiny applications: modular, tested, documented, and running somewhere other than your laptop.
Taught by Veerle Eeftink-van Leemput. Teaches production Shiny engineering.
Feeds into Shiny Sports Engineer
Outstanding UIs: CustomiZing WidgetZ
Build custom Shiny inputs, outputs, and htmlwidgets, with R talking to JavaScript over the socket.
Taught by Veerle Eeftink-van Leemput. Teaches custom Shiny component development.
Feeds into Shiny Sports Engineer
Outstanding UIs: Mobile StructureZ
Build mobile applications and progressive web apps in Shiny, with offline support and app-store deployment.
Taught by Veerle Eeftink-van Leemput. Teaches mobile and offline Shiny.
Feeds into Shiny Sports Engineer
LLM tools and AI
Building with models rather than prompting them, with evaluation attached so behavior is measured rather than asserted.
SynergiZing ML and LLMs in R
Ship a tidymodels pipeline and a retrieval pipeline with real evaluation behind it, plus a deployed model API and a working chat interface.
Taught by Nic Crane, PhD. Teaches LLM-powered tooling in R.
Feeds into LLM Engineer
Whole routes, in one go.
Compositions of the courses above rather than separate material, for people who already know which direction they are heading.
Three routes
Each bundle is the set of courses that make up one direction, taken together.
Lifetime Python Bundle
The Python route end to end: foundations, then a production machine learning pipeline.
Feeds into Production Data Scientist, Junior Sports Data Scientist
The Complete Shiny Engineering Stack
The full application route: production Shiny, custom components, and mobile.
Feeds into Shiny Sports Engineer
Becoming a BayeZian Bundle
The Bayesian route: applied modeling through to the specialist methods.
Feeds into Sports Quantitative Analyst
Every live room, the recordings, the code, and the environment, behind one door.
Join the MasterclassStart your path.
Take one course, or take the route it belongs to. Both start with the same live rooms.