- The market still hires, but fewer "generic junior" profiles and more people who combine a trade and data.
- Your previous experience is not a handicap, it is your main asset, provided you aim for the right role.
- Expect six to twelve months of serious work, a portfolio of three real projects and a network built during training, not after.
- Beware of guaranteed-job promises and salaries quoted without context.
What really changed in the market
Three years ago, a three-month bootcamp and a Power BI dashboard in a portfolio were enough to land a first analyst job. That is no longer true. The companies we support have reduced their junior hiring, because AI assistants now handle part of the simple queries and reports, and because the flow of candidates from short courses has become very large. The analyst role has not disappeared; its bar has risen a notch.
At the same time, a new demand is appearing: people who know a trade (finance, logistics, healthcare, human resources, sales) and who can handle data, ask a good question, build a reliable indicator and use AI without getting fooled. These profiles are rare and they place well.
Choosing the right target role
"Data" is not a job, it is a family. Analyst, engineer, scientist, BI specialist, data quality manager, data project manager: skills, days and salaries have nothing in common. The question is not "which job pays best" but "which job extends what I already know how to do".
| You come from | Realistic target | What makes the difference |
|---|---|---|
| Finance, controlling, accounting | Financial data analyst, BI specialist | SQL, a visualisation tool, financial modelling |
| Sales, marketing | Marketing analyst, CRM, experimentation | Basic statistics, SQL, A/B testing, marketing tools |
| Logistics, manufacturing, quality | Operations analyst, forecasting | Time series, dashboards, Python |
| Healthcare, science, research | Clinical data analyst, biostatistics | R or Python, statistics, documentation rigour |
| IT, support, networks | Data engineer, analytics engineer | Advanced SQL, pipelines, cloud, dbt |
The path that works
Foundations, no shortcuts
SQL until joins and aggregations feel natural, one language (Python most often, R in healthcare and research), descriptive statistics and the basics of inference, and a visualisation tool. Three to four months at ten hours a week. Anything promising less skips a step.
Three real projects
Not school datasets. Data from your previous sector, or from a charity, or from a small business near you with a real problem. Each project is documented: the question, the data, the method, the result, what you would do differently. A recruiter reads that with far more interest than a certificate.
The network, during and not after
Jobs are found through people. Attending meetups, publishing your projects, asking professionals for feedback, helping someone: all of this happens during training, when you have things to show and questions to ask. That is why our Academy created a volunteer network of seniors: a working professional, a few years ahead of you, who answers your questions.
A course that guarantees a job, quotes an average salary without stating the city or seniority, or whose testimonials give no company name, is selling a dream. Ask for real placement figures at six months, with job titles.
And where does AI fit?
Learn to work with an AI assistant from day one: to understand an error message, generate a query to check, document a project. But also learn to do without it, because a technical interview happens without it, and because an analyst's value lies in their judgement about the results, not in their speed at producing them. The profiles doing best are those using AI as a brilliant intern who must be proofread, not as an oracle.
"Your previous trade is your competitive advantage. Do not hide it in your CV, put it on the first line."
Conclusion
Moving into data remains a good decision in 2026, provided you aim for a role consistent with your past, accept six to twelve months of real work, build three projects that prove something and meet people before you need them. It is not a shortcut, it is a path. Those who take it clear-eyed arrive.
FAQ
Can you switch without a maths background?
For analyst and BI roles, yes: well-mastered high-school statistics are enough. For data scientist or machine learning roles, a serious refresher is needed.
Do you need a degree?
A degree reassures some recruiters, especially in large companies. A solid portfolio and a network open more doors in SMEs and scale-ups. Both together remain the best combination.
What salary to expect?
It depends heavily on the country, the city and the role. Trust no figure given without those three details. Ask working professionals about local pay scales.
How to fund a career change?
Depending on your country: personal training accounts, professional transition schemes, employment support, or self-funding with moderately priced online courses. Our Academy applies a regional rate for French-speaking Africa and offers free mentoring.
