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Learning paths/Data Analyst Foundations
Module 03 · SQL practice

SQL for data analysis.

Query a real practice database, define metrics, control joins and deliver a reproducible analytical answer.

7 published lessons365 min suggested studyLocal SQLite practice
Start the first lesson →Get the practice files ↓
The same case, new skills

Northstar Shop

From related tables
to a checked analytical answer.

2 reporting months12 verified orders
Fictional learning case · EUR · No personal data
What you will produce

A SQL analysis
someone can reproduce.

Inspect the schema, write read-only queries, control join cardinality and reconcile your measures. Complete a final analysis with an evidence note and a reproducible query script. Every lesson includes an explanation, practical task and worked solution.

Before you start: Understand Module 1’s order grain and metric definitions. Use a local SQLite-compatible editor. No paid software, account, macros or website database access is required. Window lessons require SQLite 3.25.0 or later.

Allow about six hours including exercises. The suggested times are flexible. Keep your work locally; no upload or payment is required.

Start by opening the supplied SQLite file locally. The 14 worked queries were executed against the downloadable fixture. CSVs, query starters and expected outputs are included. This website does not execute arbitrary SQL or need access to your hosting database.

A connected learning journey

7 lessons. One complete piece of work.

Explanation + practice + solution
01

Read the schema and write your first analytical query

Open the isolated practice database and identify each table’s grain.

40 min02

Filter the right population and inspect missing data

Use WHERE with explicit status, channel and date conditions.

45 min03

Calculate grouped metrics and check the denominator

Build monthly count, sales and AOV at order grain.

55 min04

Join tables without multiplying your sales

Predict join cardinality before summing order-level values.

60 min05

Use CASE and show zero activity deliberately

Build separate status counts with conditional aggregation.

50 min06

Explain comparisons with CTEs and window functions

Use a CTE to separate aggregation from comparison.

55 min07

Deliver a checked SQL analysis and evidence note

Combine safe joins and monthly metrics in one readable analytical pipeline.

60 min
Everything for the exercise

A real practice database.
A clear way to begin.

Download the ready database and read the setup guide. Work through one read-only starter at a time, then compare your results with the explained solutions.

Queries use SQLite syntax, with integer cents converted to EUR at presentation. Additional item allocations and the empty partner channel are teaching additions. All records are fictional.

Ready-to-open SQLite databaseFour fictional tables. Open a local copy in an SQLite editor.
Download ↓
14 practice startersRead-only starting queries and independent tasks.
Download ↓
Worked SQL solutions14 executed SELECT statements with reproducible outputs.
Download ↓
Setup & data dictionaryEngine, table grain, units, coverage and how to begin.
Download ↓
Alternative setup scriptCreates the fixture in a new empty local SQLite database.
Download ↓
Verified orders CSV12 orders. Monetary inputs are integer cents.
Download ↓
Imperfect source CSV13 raw rows with the four documented source defects.
Download ↓
Order items CSV24 synthetic teaching lines, reconciled to order totals.
Download ↓
Channel dimension CSVIncludes the zero-activity partner channel.
Download ↓
Expected query resultsOutput columns and values, including intentional NULLs.
Download ↓
Learner worksheetRecord query reasoning, checks and the final evidence note.
Download ↓
Before moving on

Can someone reproduce your answer?

Use the final lesson's 10-point rubric to review your work. A suggested target is 8 points, with no zero in core queries or joins and no unsupported causal claim. This is a self-review guide, not an accredited or automatically verified assessment.

Begin Module 03 →

Continue to Module 04: Data quality & exploratory analysis →

Self-learning is free. Verify your email for protected practice materials; optional support never affects access.