Your team, able to run it without us.
Taught from systems we have actually built and run, with AI woven through every pathway rather than bolted on at the end. You finish holding a portfolio piece and a certificate you can verify — not a photograph of a classroom.
Career pathways
Sequenced programmes that end with a capability rather than a certificate of attendance. Each one finishes with a capstone built on real data or a real system, so participants leave with something they made and can show an employer.
Two tracks, one curriculum
Every pathway can be taken as a core programme — the role taught the way it has always been taught — or with the AI+ layer switched on, which inserts additional sessions on working with AI in that role and the judgment to check what it produces. It is the same qualification, taught for the age of AI. Additional fee applies for the layer.
Use the switch on any pathway below to compare the two.
What you leave with
Training is only worth what it can prove. Every programme is built to end with evidence a hiring manager can check.
A portfolio piece, not a photo of a classroom
Every pathway ends in a capstone built on real data or a real system. You finish holding something you made, that a hiring manager can open and assess — the only kind of proof that survives an interview.
A certificate you can actually verify
Completion certificates carry a credential ID and a verification link, and are issued against an assessed capstone rather than attendance. If you did not build the thing, you do not get the certificate.
Credentials that live on your LinkedIn profile
The certificate adds straight into the Certifications section of your LinkedIn profile, credential ID and link included, so the proof sits where recruiters actually look.
Course catalog
Individual courses, grouped by subject. Any of them can run as a single session, be combined into a programme, or be rebuilt as a custom curriculum for your team.
Survey and field data systems
Our deepest subject. Taught from systems that ran a national survey, not from a textbook.
- ·Designing instruments that produce clean data — Question order, skip logic, validation rules, and the edit specification that has to exist before anyone builds anything.
- ·Building CAPI instruments — Hands-on construction of a tablet questionnaire, from an approved paper form to a deployable application.
- ·Running field data collection — Device fleets, sync infrastructure, supervisor monitoring, and what to do when a team is out of signal for three days.
- ·Survey data processing and tabulation — From raw sync to export-ready datasets and generated statistical tables that regenerate instead of being rebuilt.
Data quality and governance
For teams who have the data but cannot yet defend it.
- ·From messy files to a dataset you can defend — Profiling, cleaning, de-duplication, and documenting every judgment call so the result survives being questioned.
- ·Writing a codebook people will actually use — Data dictionaries, value sets, versioning, and the discipline that keeps definitions from drifting apart.
- ·Harmonizing data across rounds and sources — Making this year comparable to last year, and one office's data comparable to another's.
- ·Data privacy in practice — Records of processing, Privacy Impact Assessments, and what the Data Privacy Act actually asks of an operating team.
Analytics and reporting
Aimed at the people who have to produce the numbers, and the people who have to defend them.
- ·Dashboards people actually open — Choosing the metric, modelling underneath it, and making every figure traceable back to source.
- ·Automating the report you rebuild every month — Turning a recurring manual report into something that regenerates on demand.
- ·Reading your own numbers — A non-technical session for managers who have to present and defend figures somebody else calculated.
Technology and engineering
The stacks we build on ourselves. Taught on real projects, deployed at the end, not on toy examples.
- ·Full stack web development — TypeScript, React, and Next.js on the front, an API and a database behind it, deployed and running by the end of the course.
- ·Modern front-end development — Component architecture, styling systems, responsive layout, and accessibility that holds up when audited.
- ·Databases and SQL — Schema design, queries people can read, indexing, and the joins that most reporting actually depends on.
- ·Python for data work and automation — Scripting the repetitive parts of a data job — reading, transforming, validating, and generating outputs on a schedule.
- ·Version control and collaborative development — Git in a team: branches, review, and a history that explains itself six months later.
- ·APIs and system integration — Designing and consuming APIs, handling failure, and connecting systems that were never meant to talk.
- ·Deployment, hosting, and CI/CD — Environments, automated deployment, monitoring, and the runbook that lets somebody else operate it.
- ·The survey stack — CSPro, CSEntry, and CSWeb — The specialist toolchain behind large-scale field data collection, taught by people who run it in production.
Systems delivery and assurance
For organizations that buy systems rather than build them — and have to make sure what arrives works.
- ·Writing requirements and technical specifications — How to specify a system precisely enough that procurement buys the right thing and a vendor cannot argue later.
- ·Quality assurance and UAT for information systems — Test plans, acceptance criteria, and facilitating a user acceptance test with your own evaluator panel.
- ·Training of trainers for system rollouts — Preparing your own people to train the next hundred users, with the trainer guide and materials to do it.
Working with AI
From a first honest introduction through to the method we use on our own engagements — including where it does not pay off.
- ·AI Essentials — What these tools actually are and are not, how to ask for something useful, how to check what comes back, and what must never be pasted into them in the first place.
- ·Practical AI for operations teams — Where AI genuinely removes work, where it quietly adds risk, and how to keep a human accountable for the output.
- ·An AI-assisted delivery workflow — Planning, drafting, testing, and documenting with AI agents under review — the working method, installed in your team.
- ·Agentic engineeringBy request — Designing systems where agents plan, use tools, and act across multiple steps — with review gates, evaluation, and a clear accountability model. For teams already working with AI every day.
Digital marketing
Practical channel skills for teams who have to market on a small budget and prove it worked.
- ·Search engine optimization (SEO) — Technical health, on-page structure, keyword intent, and content that ranks because it deserves to.
- ·Paid advertising on Google and Meta — Campaign structure, audience targeting, creative testing, and keeping a budget under control.
- ·Social media management in practice — Content calendars, scheduling, community response, and what to do when a post goes badly.
- ·Content marketing and copywriting — Writing for a specific audience and a specific action, rather than writing to fill a page.
- ·Email marketing and automation — Lists, sequences, deliverability, and the automations that run without anyone remembering to send them.
- ·Marketing analytics and attribution — Conversion tracking installed correctly, and honest reporting on which channel actually produced the enquiry.
- ·AI for marketing content and campaigns — Research, drafting, and creative variation at volume — with the editorial checks that keep a brand sounding like itself.
How it’s delivered
The same teaching, bought three ways. Training is quoted per engagement — the course, the cohort size, and whether we build a custom curriculum all move the number, so we would rather scope it with you than publish a figure that fits nobody.
How a course gets built
We find the real gap
A scoping call to establish what your team is actually stuck on, rather than what a generic syllabus assumes.
We build it on your data
Exercises use your own files and systems, so what people practise is what they go back to on Monday.
You keep everything
Workbooks, exercise files, and — on a programme — the trainer guide, so you can run it again without us.
Contact
Tell us who needs training and what they need to be able to do afterwards. We’ll tell you which pathway or course fits — and if a half-day would do it instead of a programme, we’ll say that.