Do I need to know programming?

Not always at entry level. Many junior roles mainly use spreadsheets, SQL and a visualisation tool. Learning a language such as Python often helps later, especially for larger datasets or automated reports.

What is the difference between a data analyst and a data scientist?

Analysts usually focus on describing what happened and why, using reports and clear analysis. Data scientists more often build predictive models and work with machine learning. The titles overlap, and duties vary between employers.

How can I get experience without a job?

Use public datasets, volunteer to analyse data for a student society or local organisation, or improve a report in your current role. For each project, explain the question, the method and the result.

Career guide

How to become a data analyst

Data analysts turn raw information into answers that people can act on. They collect and clean data, look for patterns and explain what they find in clear charts and plain language, helping teams in sales, finance, operations and many other areas.

The role suits curious people who enjoy both numbers and communication. Many analysts come from business, economics, science or engineering backgrounds, and employers often value practical skills in spreadsheets, SQL and visualisation as much as a specific degree.

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What the work looks like

Pulling data from databases and spreadsheets with SQL or other tools
Cleaning data by fixing duplicates, gaps and inconsistent formats
Building reports and dashboards that track key measures
Analysing trends and testing simple explanations for changes
Presenting findings to colleagues and answering follow-up questions

First roles

Junior data analyst
Reporting or business intelligence analyst
Operations or marketing analyst
Data analyst intern or graduate trainee

How to start

Get confident with spreadsheets, including formulas, lookups and pivot tables; try the Excel test.
Learn SQL to query databases, then check your level with the SQL test.
Study basic statistics so you can tell a real pattern from random noise.
Practise on public datasets and write short reports explaining what you found.
Show your work in a small portfolio and prove core skills with the data analysis test.
Highlight tools and measurable results in a CV made with the CV builder.

Skills employers look for

Prove these with a free test, or read the guide for each one.

Data AnalysisFree test Guide
StatisticsFree test Guide
SQLFree test Guide
ExcelFree test Guide
PythonFree test Guide
AnalysisFree test Guide
Critical ThinkingFree test Guide

Your CV for each market

Articles

Excel Skills Employers Expect: Basic to Advanced

Common questions

Do I need to know programming?: Not always at entry level. Many junior roles mainly use spreadsheets, SQL and a visualisation tool. Learning a language such as Python often helps later, especially for larger datasets or automated reports.
What is the difference between a data analyst and a data scientist?: Analysts usually focus on describing what happened and why, using reports and clear analysis. Data scientists more often build predictive models and work with machine learning. The titles overlap, and duties vary between employers.
How can I get experience without a job?: Use public datasets, volunteer to analyse data for a student society or local organisation, or improve a report in your current role. For each project, explain the question, the method and the result.