Short answer
Start with spreadsheets and SQL, learn one business intelligence tool, then build a small portfolio that shows how you clean data, investigate a question and communicate a finding. Add Python when it supports your goals.
What does a data analyst do?
A data analyst turns raw information into useful evidence. Depending on the team, the work can include collecting and cleaning data, checking definitions, querying databases, creating reports, spotting changes and explaining what the results mean.
Good analysis begins with the decision or question. The tools matter, but so do accuracy, context and the ability to explain limitations clearly.
Skills to learn first
| Skill | What it helps you do | Priority |
|---|---|---|
| Spreadsheets | Clean, sort and summarise data; use formulas and pivot tables. | Start here |
| SQL | Retrieve, filter, join and aggregate data stored in databases. | Core skill |
| Power BI or Tableau | Build visual reports and dashboards around a business question. | Core skill |
| Communication | Explain findings, assumptions and next steps to other people. | Use throughout |
| Python | Automate repeat work and explore larger or more complex datasets. | Add next |
A step-by-step learning plan
Get comfortable with spreadsheets
Practise sorting, filtering, formulas, lookups, pivot tables, data checks and basic charts with tidy and messy datasets.
Learn SQL by answering questions
Work through SELECT, WHERE, GROUP BY, JOIN and window functions. Explain what each query returns, not only how to write it.
Choose one dashboard tool
Learn to shape a small dataset, select useful measures and design a report that helps someone make a decision.
Build complete portfolio projects
Choose a question, document data cleaning, show your analysis, present a visual result and write a short plain-language conclusion.
Prepare for applications
Describe your projects clearly, tailor your CV to the role and practise explaining your process and trade-offs.
How long does it take?
There is no single timeline. Your starting point, available study time and practice routine all matter. Set a weekly schedule you can sustain and review progress by what you can do: clean a dataset, write useful queries, build a report and explain the result.
What makes a useful portfolio?
- Use a clear question that a real organisation might care about.
- Describe where the data came from and any important limitations.
- Show the cleaning and analysis steps so your work can be followed.
- Include a visual or dashboard that supports the conclusion.
- Write a short summary of what you found and what you would investigate next.