A data analyst turns raw data into answers people can act on. It is one of the most accessible technical careers for students from any background. Here is a roadmap you can follow alongside your degree.
Stage 1: Foundations (weeks 1–6)
- Spreadsheets. Formulas, lookups, pivot tables and charts. Many analyst jobs still start here.
- Basic statistics. Mean, median, spread, distributions, correlation vs causation.
- Asking good questions. What decision will this analysis support?
Stage 2: SQL (weeks 6–12)
SQL is the most important analyst skill. Learn SELECT, WHERE, GROUP BY, JOINs, subqueries and window functions. Practise on public datasets until you can answer a business question in a single query.
Stage 3: Visualisation (weeks 12–16)
Learn one BI tool well, such as Power BI, Tableau or Looker Studio. Focus on clarity: the right chart, clean labels and one message per chart.
Stage 4: Python for analysis (weeks 16–24)
Learn pandas for cleaning and reshaping data and a plotting library for charts. You do not need machine learning for most analyst roles.
Stage 5: Projects
Build three portfolio projects, each answering a real question:
- A sales or finance dashboard in a BI tool
- An SQL analysis of a public dataset with a written summary
- A Python notebook that cleans a messy dataset and finds insights
Stage 6: Internship and portfolio
Put projects on GitHub or a simple portfolio site, and apply for analyst internships. Use our first internship guide.
Stage 7: Interviews
Expect an SQL test, a spreadsheet or case task, and questions like "Walk me through a project" and "How would you measure X?" Practise explaining findings in plain language.
See the full path in our career roadmap tool.