Career Programs
Become a Production Data Scientist
Analysts moving from notebooks to pipelines, packages, and deployed systems that survive contact with production.
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.
16 weeks, week by week.
Not a reading list. Each phase carries the skills you work on, what exists at the end of it, and what you can do by then.
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
- Mentor one-to-one every second week, on your own work
Hand your environment to someone else and have it work first time.
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
- Mentor one-to-one every second week, on your own work
Put a model behind a schedule and know within an hour when it breaks.
Capstone and Film Room
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
- Mentor one-to-one every week through the capstone
Hand a running system to another engineer with nothing but the repository and the runbook.
Dates are not set yet. The waitlist is where they are announced, and it is one message when they land.
Join the waitlistPractice 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.
What that means literally: AI coaching built into practice sessions, stakeholder simulation where you present to a general manager and defend a decision to a director, and no-code visual builds alongside code builds, because plenty of the work is a sketch or a plan rather than a script.
AI as copilot, not autopilot. We teach you to direct the machine and defend the output. The part that is scarce 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.
Practice worlds, built from the course itself.
Between the live sessions and the mentor calls, this is where the repetition happens. Not a quiz: the world is generated from the course's own code and data.
Interactive practice worlds built from each course's own code, data and recordings. Every quantity computed live from the source data, every instructor quotation verified against the recording, and no exercise published unless its reference solution runs and passes its own tests.
- Built from the course's own code and data, not a generic exercise set
- Quantities computed live from the source, so a number on screen is the number in the data
- Nothing published until its reference solution runs and passes its tests
The roles this work is hired for.
Named because they are the roles the skills above are used in, not because we are promising you one of them.
These are the roles the work in this program is used in. We describe what you build and what you can do with it, and we do not promise a job, a placement, or an interview.
A clock, a real task, and a room that pushes back.
The part most programs describe and never show. Here is how it actually runs, and the kind of question it runs on.
Practice runs in a timed provisioned environment, the same one the sessions run in. 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?
The practice sessions are the part people underestimate and then say was the reason it worked. They are in this program from the first week.
Join the waitlistThen take the method and leave the sport.
Sports is the hook. Pipelines that run on a schedule, fail loudly, and can be handed over are the method, and every organization shipping models has the same problem.
- 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
The spine that does not change by phase.
One-to-one mentor sessions on your own work, fortnightly through the taught weeks and weekly through the capstone. Simulated technical interviews, recorded and reviewed. Front-office presentation practice, where a recommendation meets a room that pushes back. The Film Room once a month, alternating portfolio teardowns and interview preparation from the hiring side. See The Film Room
Production is the live track running alongside the program, so the weekly sessions and the game plan are pulling in the same direction rather than competing for your evenings.
This program is a defined credential pathway culminating in a verifiable digital badge issued through Credly, a Pearson company. We provide completion records. We are not a degree-granting or accrediting institution. If a formal credential is a requirement for you, this complements an accredited pathway rather than replacing it.
Join the waitlist.
This program is not open yet. The waitlist is the only place we announce dates, and it is one message when they land.
Join the waitlist
We will write once, when this program opens, with the dates and the application. No other list, no other mail.
Form not loading? Email [email protected] with the program name in the subject and we will add you by hand.
Every live room, the recordings, the code, and the environment, behind one door.
Join the MasterclassSee how it is taught first.
Sit in on a live session. It is the fastest way to judge any of this.