Introduction to the data analyst profession
The data analyst transforms raw data into actionable insights to guide strategic decisions. An expert in statistics and visualisation, they collect, process and analyse large volumes of information to identify trends and opportunities.
Unlike the Data Scientist who develops complex predictive models, the analyst focuses on descriptive and diagnostic analysis: what happened? Why? They use SQL, Python or R, and visualisation tools (Tableau, Power BI, Looker) to communicate insights to business teams and management.
Main responsibilities
Data collection and preparation
Identify relevant data sources (databases, APIs, Excel files, CRM). Extract data via SQL or Python. Clean and structure datasets (missing values, deduplication).
Statistical analysis and exploration
Perform descriptive analysis (averages, medians, distributions). Identify correlations and patterns. Conduct segmentation analysis. Test hypotheses.
Visualisation and reporting
Create interactive dashboards with Tableau, Power BI or Looker. Produce reports for stakeholders. Automate recurring reporting.
Support for decision-making
Translate insights into actionable recommendations. Present analyses to business teams and management. Measure the impact of decisions made through data.
Continuous improvement and data quality
Ensure data quality and reliability. Identify opportunities to optimise analytical processes. Train teams in dashboard usage.
Required skills
Technical skills vs Soft skills
- SQL proficiency for querying databases
- Statistics skills (averages, standard deviations, correlations)
- Advanced Excel expertise (pivot tables, complex formulas)
- Data visualisation tool mastery (Tableau, Power BI, Looker)
- Python (pandas, numpy) or R basics for analysis
- Knowledge of ETL tools and databases (PostgreSQL, MySQL, BigQuery)
- Excellent communication and data storytelling skills
- Analytical mindset and methodological rigour
- Curiosity and ability to ask the right questions
- Attention to detail and precision
- Ability to synthesise complex information
- Autonomy and proactivity in seeking insights
Data Analyst vs Data Scientist: what's the difference?
| Critère | Data Analyst | Data Scientist |
|---|---|---|
| Type of analysis | Descriptive & diagnostic | Predictive & prescriptive |
| Key question | What happened? | What will happen? |
| Main tools | SQL, Excel, Tableau/Power BI | Python, R, ML frameworks |
| Maths required | Basic statistics | Advanced statistics, ML |
| Average salary | 35-55K EUR | 45-70K EUR |
Training and career development
Training for data analyst
| Level | Qualification | Opportunities |
|---|---|---|
| Bac+3 | Mathematics, Statistics, Economics Degree | Junior data analyst |
| Bac+3 | Bachelor Data Analytics, Business Intelligence | Junior business analyst |
| Bac+5 | Master Data Science, Statistics, Econometrics | Confirmed data analyst |
| Bac+5 | Engineering school (ENSAE, Telecom, Centrale) | Senior analyst, Data Scientist |
Junior analyst
Descriptive analysis, reporting, tool learning.
Confirmed analyst
Complex analysis, project autonomy.
Senior analyst
Data project leadership, mentoring juniors.
Lead Data Analyst / Analytics Manager
Team management, analytics strategy.
Head of Data / Chief Data Officer
Company data strategy.
Salary scale 2026
Data analyst salary (annual gross)
| Experience | Salary | Total package | Île-de-France |
|---|---|---|---|
| Junior (0-2 years) | 32-40K EUR | 35-43K EUR | +10-15% |
| Confirmed (2-5 years) | 40-50K EUR | 43-53K EUR | +10-15% |
| Senior (5-10 years) | 50-65K EUR | 53-70K EUR | +15-20% |
| Lead / Manager (10+ years) | 65-85K EUR | 70-90K EUR | +15-20% |
Tools mastered by Data Analysts in 2026
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