Skills
Preparing and validating a dataset
An analysis is worth exactly what its input data is worth. Cleaning, reconciling, deduplicating, documenting: this is the work that takes up most of a data professional's time, and it decides how much trust every downstream figure earns.
The work that makes a figure defensible
Two departments announcing two different figures for the same reality: the situation is commonplace, and it almost always has the same cause — implicit preparation rules known to one person only. The skill consists in making those rules explicit: which source prevails, how two identifiers are matched, what happens to a missing value, when a row is rejected. Once written, the rules can be checked, replayed and handed on — and the figure becomes defensible in a meeting.
- Set the cleaning rules for a source and make them reproducible
- Reconcile two reference systems that use different identifiers
- Put automatic quality checks on an incoming data flow
- Document the provenance and definition of every indicator produced
The blocks that build it
The skill is built block after block, in the order of the years. You see here exactly where it is taught, and for how many taught hours.
| Teaching block | Programme | Taught hours | Year |
|---|---|---|---|
| Data quality and preparation | Business Intelligence and Steering | 200 h | Year 1 |
| Data engineering | Data Science programme — Bac+5 level | 240 h | Year 1 |
| Cleaning and preparing data | Certificate in Artificial Intelligence for Business Decisions | 40 h | Year 1 |
| Databases and querying | Business Intelligence and Steering | 240 h | Year 1 |
Which positions it leads to
At levels 6 and 7, professional experience is part of the entry requirements.
| Professional qualification | Target occupation | Level | Taught volume |
|---|---|---|---|
| Data and Annotation Operator | data operator, annotator, dataset preparer | 4 · Skilled employee and operative | 600 h |
| Data and Applied AI Technician | data technician, AI solutions integrator | 5 · Senior technician and supervisor | 700 h |
| Data Analyst | data analyst | 6 · Manager and professional | 750 h |
The level you reach
You take on a raw source and deliver a controlled, documented dataset that replays identically. Level shows in volumes as much as in titles: here are both, side by side.
- teaching blocks
- 4
- of taught instruction
- 720 h
- credits in total
- 72
- professional qualifications concerned
- 3
Catalogue rule: one credit is twenty-five hours of work, ten of them taught.
The available entry points
The level shown indicates the award targeted; the duration, the real commitment in months of taught work.
| Programme | Level | Duration | Fees |
|---|---|---|---|
| Business Intelligence and Steering | Bachelor's degree | 2 years | 5 000 000 GNF per year |
| Data Science programme — Bac+5 level | Master's degree | 2 years | 7 200 000 GNF per year |
| Certificate in Artificial Intelligence for Business Decisions | Professional certification | 5 months | 3 600 000 GNF in total |
Before you decide
Is this skill relevant to someone outside computing?
It is first of all relevant to those who produce figures: management control, operations, human resources, compliance. The four-block certificate was designed for them.
What tooling supports the coursework?
Spreadsheets for everyday volumes, SQL and Python for larger ones. The reasoning taught applies to all three, which makes the choice of tool secondary.
How much of a real project does preparation take?
Most of the schedule, and the programme reflects that: 200 taught hours go to it, against 160 for modelling. That is a deliberate curriculum choice.
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