Careers
Data analyst
Turning raw data into decisions: the analyst builds the indicators, explains the variations and answers the questions management actually asks. A job of curiosity, rigour and clarity of expression.
One question, one defensible answer
Everything starts from a business question, rarely well framed at the outset: « why are sales falling in that region ». The analyst reframes it, identifies the available data, checks its reliability — the longest and most useful step. Then comes the analysis itself, then the delivery, which decides the work's real effect: one readable chart and three sentences beat a fifty-column table. Dashboards then automate what repeats.
- Reframe a business question into a workable analysis
- Extract, join and validate data from several sources
- Build a defensible indicator with a documented calculation
- Present a result to decision-makers and propose an action
Who employs analysts
Financial services
Loan portfolios, payment behaviour, risk: large volumes and questions asked every week.
Telecoms and retail
Usage, churn, performance by outlet: abundant data directly tied to revenue.
Organisations and programmes
Indicator tracking, effect evaluation, accountability to funders: a growing and demanding need.
The programme data
Four values, one source. They feed the qualification page, the application form, the equivalence file and the final certificate without ever being re-entered. That single source is what makes two catalogue programmes comparable: the figures carry the same meaning and the same unit everywhere.
- qualification level
- 6
- months of programme
- 10
- taught hours
- 750 h
- credits
- 75
- competency blocks
- 5
Manager and professional
Led by an instructor.
1 credit = 25 h of work, 10 of them taught.
Framework of the « Data Analyst » qualification, code TP27.
What each of the blocks covers
For each block the framework states the ground covered and the taught hours devoted to it. That precision serves at the orientation stage, then during the programme, then on exit when you have to describe what you can do. One vocabulary throughout, that of the occupation as companies practise it.
- Data preparation and quality
- 190 (25 %)
- Analysis and modelling
- 200 (27 %)
- Reporting and decision support
- 180 (24 %)
- Data governance and ethics
- 130 (17 %)
- Professional conduct
- 50 (7 %)
- Data preparation and quality · 190 h — Sources, extraction, cleaning, consistency, documentation, reproducibility
- Analysis and modelling · 200 h — Applied statistics, segmentation, forecasting, interpretability
- Reporting and decision support · 180 h — Dashboards, narrative, communicating uncertainty, steering committees
- Data governance and ethics · 130 h — Lifecycle, protection, bias, responsible use
- Professional conduct · 50 h — Refusing an analysis the data does not support, dealing with the requester
Becoming a data analyst
This table sets side by side what genuinely prepares for this role. Credits and taught hours measure the gap between two options; fees relate that gap to a budget. Those are the three questions a candidate asks, and the three answers sit on a single row.
| Programme | Duration | Credits | Taught hours | Fees |
|---|---|---|---|---|
| Data Science programme — Bac+5 level · Master's degree | 2 years | 120 | 1200 h | 7 200 000 GNF per year |
| Business Intelligence and Steering · Bachelor's degree | 2 years | 120 | 1200 h | 5 000 000 GNF per year |
Three questions, three precise answers
How does this differ from management control?
Management control works on the company's financial framework; the analyst works on all available data, including operational and commercial. The business intelligence and steering programme bridges the two approaches.
Is programming required?
The database query language is the foundation, and it is acquired quickly. An analysis language opens heavier processing and reproducibility, and the data science programme installs it fully.
Does this lead to a data leadership role?
The chief data and AI officer qualification, at level 7, is its culmination: governance, data assets, a portfolio of use cases and investment arbitration.
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