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How to become a Data Analyst

Turn messy data into decisions: SQL, statistics and visualisation that a business actually acts on.

13-19 weeks4 phases13 steps17 free resources2 checkpoint tests

Best for

Students who enjoy finding patterns and explaining them, and want a data career without a heavy engineering build-up.

Maybe not for you if

If you want to build the systems and pipelines rather than answer questions with data, Data Engineer suits you better.

The 4 phases

What each phase gets you to. The steps, resources and checkpoint inside each one open with a free account.

  1. Phase 1 - SQL until it is boring

    4-5 weeks

    Goal: Write multi-table queries with joins, grouping and window functions without help, on a real database you installed yourself.

    3 steps · ends in a scored checkpoint (65% to clear) · assumes basic computer literacy

  2. Phase 2 - Statistics you can defend

    3-4 weeks

    Goal: Summarise a dataset honestly and know when a difference between two groups is just noise.

    3 steps · ends in a build deliverable

  3. Phase 3 - Excel, Python and visualisation

    4-6 weeks

    Goal: Clean a messy dataset in Excel or Python and ship a chart or dashboard that makes one point a manager acts on.

    4 steps · ends in a build deliverable

  4. Phase 4 - Clear the process

    2-4 weeks

    Goal: Pass the written round, explain your analysis to a non-technical listener, and put both on one page a recruiter believes.

    3 steps · ends in a scored checkpoint (65% to clear)

Open the full Data Analyst roadmap

Free account, no card. It takes about a minute and you do not need to verify your email to start.

  • All 13 steps, in order, with why each one is there
  • 17 hand-picked free resources, no paid course upsells
  • The specific mistake people make at each stage
  • 2 scored checkpoint tests, so progress is earned not ticked
  • Progress saved per step, so a break does not cost you the thread
  • Adaptive start, phases your test history already clears are skipped
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What hiring actually looks like here

Analyst processes typically screen on SQL first and communication second. Most include a live SQL exercise and some form of 'explain this to a non-technical manager' question.

We claim no placement outcome, salary or success rate. This describes what is commonly reported about the role, nothing more. Linked resources are credited to their authors.

Comparing paths?