Programs

Production data science.

Analysts moving from notebooks to pipelines, packages, and deployed systems that survive contact with production.

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Opens later16 weeks · Foundations to Applied pipelines to Capstone

No job is guaranteed. We do not promise placement, an interview, or an offer.

Is this you

Three things people say before they start.

If none of these are familiar, this is probably not the right program, and the call will tell you that faster than the page will.

  • Your model works on your machine.
  • Something broke overnight and you heard about it from a person, not from a page.
  • You are the reason the pipeline runs, and you cannot take a week off.

This is a real session, not a promotional cut. Production runs live alongside the program.

The plan

16 weeks, week by week.

Each phase names the skills you work on, what exists at the end of it, and what you can do by then.

Languages and tools across the program
PythonRDocker
Weeks 1 to 4

Foundations

Clean, reproducible analysis before any of it gets automated.

 In these weeks
  • Python and pandas, structured for reuse
  • Environment and dependency management in both languages
  • End-to-end git workflow: branches, review, and QA gates before anything lands
What exists at the end
A reproducible analysisA locked environment
By now you can

Hand your environment to someone else and have it work first time.

Weeks 5 to 10

Applied pipelines

The artifact that gets screened for hardest, built properly.

 In these weeks
  • Machine learning pipelines in scikit-learn
  • Honest model evaluation and selection
  • Containers, CI, and the QA gates a change clears
  • Deploy and monitor: scheduling, logging, failing loudly
  • Simulated technical interviews, recorded and reviewed
What exists at the end
A production pipelineA CI configuration
By now you can

Put a model behind a schedule and know within an hour when it breaks.

Weeks 11 to 16

Capstone

A system someone else can own, then the rooms it has to survive.

 In these weeks
  • Capstone pipeline running on a schedule
  • Monitoring, alerting, and a runbook
  • AI orchestration training: planning work for delegation, delegated builds, browser QA
  • Front-office presentation practice: the recommendation, and the pushback
  • Simulated technical interviews, recorded and reviewed
What exists at the end
The deployed pipelineA runbookA portfolio writeup
By now you can

Hand a running system to another engineer with nothing but the repository and the runbook.

Not sure which phase you would start from? That is the first thing the call works out.

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Inside the program

The 6 sessions and courses it is built from.

Each one is on sale on its own, as a recording you buy once, on its own page. The program is the order, the weekly work around them and the capstone at the end.

Sessions 4

Async masterclass · 75 min IT RUNS ITSELF Game-Day Automation Jasmine DalyPrincipal Consultant @ Daly Analytics Available now After this you can schedule a GitHub Actions workflow that pulls, processes, reports and commits without you.
Async masterclass · 75 min KICKOFF TO PRODUCTION Kickoff to Production in Minutes Stefan LinnerFounder & R Developer @ LinneRProgramming Available now After this you can take a sports analytics Shiny app from a blank folder to production with Claude Code doing the plumbing.
Async masterclass · 75 min FILL THE GAPS The Injury Report Problem Greg MatthewsPhD · Director, Center for Data Science and Consulting @ Loyola University Chicago Available now After this you can run a complete multiple-imputation workflow on a sports dataset full of holes and pool the results correctly.

Courses 2

Course · 43 lessons PREDICT THE WIN. Python MachineZ: ML in Sports Patrick McFarlaneDirector of Predictive Modeling @ Philadelphia Phillies Available now After this you can build and honestly evaluate the machine learning models teams use to project performance.
Course · 56 lessons ZERO TO DRAFT DAY BreeZing Through the Tidyverse Paul SabinPhD · Co-Founder @ Sage Sports Group Available now After this you can turn raw NFL, NBA or MLB data into an R report that reruns and gives the same answer.
The Coaching Room

Practice with somebody on the other side of the table.

AI coaching is built into the practice sessions. The coach takes the stakeholder's part, and you work the problem in whatever medium the question actually calls for.

AI as copilot, not autopilot. You learn to direct the machine and defend the output. The scarce part is not producing an answer; it is knowing whether the answer is right and being able to say why in a room that will push back.

Simulated practice

A clock, a real task, and a room that pushes back.

How it actually runs, and the kind of question it runs on.

Practice is timed. You get a real task and a clock, not a quiz. The session is recorded, and the review afterwards is about the reasoning you showed rather than whether you reached the expected answer.

Scenarios are written the way the question actually arrives
  • The nightly job failed at 3am and nobody noticed until lunchtime. What do you change?
  • This model was trained eight months ago. How would you tell whether it is still any good?
  • You are handing this pipeline to another team on Friday. What is in the handover, and what will they call you about anyway?
Questions

The ones people ask first.

Will I get a job at the end?

No job is guaranteed. We do not promise placement, an interview, or an offer. What the program promises is the work: a capstone you can defend, a portfolio with real builds in it, and interview practice that is recorded and reviewed.

What does it cost?

There is no public price and no checkout on this page. Cost, dates and where you would start are settled on a short call, and the honest answer is sometimes that a single session or course is the better start.

Which weeks would I start from?

That is the first thing the call works out. The plan on this page is the whole route; a person who already has the foundations starts further along it.

Not into sports?

Then take the method and leave the sport. Pipelines that run on a schedule, fail loudly and can be handed over are the method. The same runbook fits these fields:

  • Retail Demand forecasting that has to be right every night, not once
  • Logistics Routing and capacity models running against the clock
  • Energy Load forecasting where a stale model is an expensive model
  • Any org shipping models The work is the same wherever the data comes from
What runs alongside the taught weeks?

Simulated technical interviews, recorded and reviewed. Front-office presentation practice, where a recommendation meets a room that pushes back. AI orchestration: planning work for delegation, delegated builds, and the checks that catch what a model got wrong.

Production is the live track running alongside the program, so the weekly sessions and the plan pull in the same direction.

Next step

Ask.

There is no public price and no checkout on this page. A short call settles cost, dates and where you would start, and if a single session is the better start we will say so.

No job is guaranteed. We do not promise placement, an interview, or an offer.

Ask about this program
Keep up

One email when something new arrives.

First name and email. One message when a session recording, a skill, a cheat sheet or a posting worth your time arrives here, and no other mail. Replies reach a person.