Data analyst roadmap 2026

Identify the decision, audience, unit of analysis and definition of success.

Build: Ask which campus events should run again based on attendance and costs.

Review: Define attendance rate and distinguish registrations from check-ins.

Understand missing values, duplicates, inconsistent dates and outliers. Preserve the original input.

Build: Clean a synthetic event CSV and record every transformation.

Review: Report rows before and after cleaning and justify exclusions.

Learn filtering, grouping, joins and window functions. Think about table grain before joining.

Build: Calculate attendance by month and event type.

Review: Check that a join has not accidentally multiplied records.

Use pandas for repeatable transformations. Keep business definitions in one place.

Build: Reproduce your spreadsheet results in a script or notebook.

Review: Reconcile totals and explain differences rather than hiding them.

Choose charts around comparisons. Label axes, units and time ranges; avoid misleading scales.

Build: Create a bar chart of attendance rate and a monthly trend.

Review: A reader can identify the denominator and whether samples are small.

Separate observations from causal claims. Include uncertainty and alternative explanations.

Build: Recommend two events to repeat with a one-page evidence memo.

Review: State what additional data would change your recommendation.

Your portfolio outcome

Campus events analysis with a synthetic dataset, cleaning log, SQL queries, dashboard and decision memo.