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Data Analyst: role, skills, tools and learning path

Understand the role, see what Data Analysts actually do at work, and identify the skills to build.

Who is this article for?

For people considering a Data Analyst career, moving into data, or wanting to understand the skills actually used in the role before choosing a training programme.

What you need to understand

A Data Analyst turns a business question into an analysis process. The work does not start with a tool: it starts with the problem, the available data, the useful indicators and the expected level of reliability.

Key foundations include data quality, granularity, table relationships, indicators, aggregations, visualisation and communication. The goal is not to produce more numbers, but to produce information that is useful, controlled and understandable.

Why this skill matters today

Organisations have access to growing volumes of data, but that data only creates value when it can be trusted, interpreted and connected to a real decision. The Data Analyst sits at the intersection of business understanding, analysis, technical tools and communication.

The market should be interpreted by sector, experience level and location. The key is to connect employment trends with skills that can genuinely be used in business.

What you actually do with this skill

Analyse salesIdentify the products, customers, channels or regions behind a change in revenue or margin.
Build management indicatorsTurn operational data into understandable, controlled KPIs.
Detect anomaliesSpot duplicates, inconsistencies, missing values, trend breaks or unusual results.
Create dashboardsGive teams a clear interactive view with Excel, Power BI or other BI tools.
Support decisionsTurn quantified observations into reasoned recommendations, including assumptions and limits.

Concrete example: analysing a margin decline

A company sees revenue increase while margin falls. The Data Analyst must understand why.

Problem : Margin is falling despite higher sales.

Context : Several products, regions, channels and discount policies.

Skills : SQL, data quality checks, aggregations, margin analysis and visualisation.

Example : Compare revenue, cost, discounts and margin by product, customer, region and period.

Hands-on application : Build a dashboard, identify the segments driving the decline and produce an actionable recommendation.

Skills to master

Data Analysis is not one tool. It combines business framing, data quality, querying, preparation, modelling, analysis, visualisation and communication.

BeginnerUnderstand the role, frame a question, work with simple datasets, use Excel and produce basic indicators.
IntermediateQuery databases with SQL, prepare data with Power Query, build an analytical model and create Power BI dashboards.
Advanced / autonomousControl quality and granularity, design DAX measures, automate analyses, document results and defend recommendations with stakeholders.

Errors, limitations and points of attention

  • Starting analysis without understanding the decision to support.
  • Confusing correlation with causation.
  • Ignoring missing values, duplicates or inconsistent data.
  • Mixing granularities and causing double counting.
  • Choosing a KPI because it is easy to calculate rather than useful.
  • Producing an attractive but misleading chart.
  • Presenting a result without assumptions, limits and checks.

How to progress

BeginnerUnderstand the role, data, indicators and basic controls.
IntermediateBuild SQL, multi-source preparation, Power Query, modelling and Power BI skills.
AdvancedRun end-to-end analyses, automate, document, control and communicate at a professional level.
Mandragore Learning Academy

Train with Mandragore Learning Academy

The professional Data Analyst training path is structured to progressively build job skills: framing, data quality, Excel, SQL, joins, Power Query, modelling, DAX, Power BI, advanced analysis and communication.

It answers the question “how can I learn the Data Analyst role through a structured programme?” with a progression organised into courses and lessons.

View the Data Analyst training path →
Related competency path

Related competency path

The Data Analyst competency path shows the essential, recommended and complementary skills around the role. It gives a broader view of the skills ecosystem beyond the Data Analyst training path itself.

View the Data Analyst competency path → Become a Data Analyst: understand the role and skills →
Sources and references

Mandragore source: config_metiers.php remains the source of truth for the courses and skills actually taught.