AI data annotator and preparer
Build and annotate the datasets a model needs in order to learn.
- Block code
- DATA-ANNOT
The format
Three months, two hours a day.
- Certificate programme · 128 h
- Two hours a day
- 250,000 F — the same fee for all sixty
- All in the same format, at the same fee.
The qualification
A CQP under MINEFOP accreditation.
- Leads to the CQP — Certificat de Qualification Professionnelle, which the ministry used to call the AQP.
- Awarded under MINEFOP accreditation.
- The full programme for the same profession remains open to anyone aiming for the complete qualification.
Sessions
A new session opens every quarter.
Next intake: Monday 5 October, with enrolment open until 2 October. The following one starts on 4 January 2027.
- First-quarter intake — enrolment closes on 2 October5 Oct.
- Second-quarter intake — 20274 Jan.
Objectives
What you will be able to do.
Understand each stage of the training data life cycle and who is involved
Annotate text, images and audio methodically with a professional tool
Measure agreement between annotators and resolve a disagreement
Write an annotation guide that a third party can apply without calling you
Clean a dataset: duplicates, missing values, outliers
Anonymise or pseudonymise according to what the intended use allows
Deliver a versioned dataset with its datasheet and terms of reuse
Programme
Four modules.
Module 0132 h Module 0232 h Annotating text, images and audio methodically
Module 0332 h Quality, inter-annotator agreement and guidelines
Module 0432 h Cleaning, anonymising and delivering a dataset
Who it is for
Who it is aimed at.
- Complete beginners. No technical prerequisites: the block starts from what training data is. It is the way into the sector, and the first job open to people who cannot program.
Afterwards
Where it leads.
- Data annotator, data preparer, data quality assistant. Natural next step towards data engineering (Data engineer — pipelines and data quality) or analysis (AI-augmented data analyst).
Method
How it works.
Systematic alternation between short theory sessions and practical work, with most of the time spent on practice
Progression from simple to complex: each module builds on what was learnt in the previous one
Realistic role-plays drawn from Cameroonian business cases
Considered use of generative AI as a working tool, in line with the common core
Regular formative assessments and a final integrative project drawing on all four modules
Assessment
How you are assessed.
Formative assessment at the end of each module (graded role-play and practical exercises)
Practical work assessed against competency sheets
Final integrative project, presented and defended before the trainer
One graded assignment per module in the online course space
Tools
What you work with.
Label Studio or CVAT — text, image and audio annotation
Spreadsheet and basic Python for cleaning
Audacity for audio segmentation
Git and object storage for dataset versioning
Public datasets and anonymised company datasets
Keep exploring
In the same field.
Join the next session
Ready to get started?
Sessions open every quarter. Apply now or request the detailed brochure — our advisers will get back to you.