Data Analytics Plugin Learning Lab

This lab uses the Data Analytics plugin's bundled synthetic product-growth dataset. It is designed to show the full workflow without requiring a live warehouse or exposing real business data.

What's included

• sample-product-growth.csv — 32 source rows at one acquisition channel per week
• analysis.sql — the durable SQL used for weekly and channel-level analysis
• product-growth-analysis.ipynb — an executed, reader-friendly analysis notebook
• analysis-results.json — reviewed query outputs and data checks
• artifact.json — the canonical interactive report payload
• build_lab.py — rebuilds the notebook, results, and artifact from the CSV

Rebuild the lab

From this folder:

python .\build_lab.py

The script uses only Python's standard library and SQLite. It verifies the expected row, week, and channel counts; reconciles weekly signups; executes the notebook cells in order; and regenerates the report payload.

Prompts to try

Paste one of these into a new Codex task and attach or reference sample-product-growth.csv:

1. @Data Analytics diagnose why activation fell while signups grew.
2. @Data Analytics compare acquisition channels and recommend where to invest.
3. @Data Analytics validate this dataset and identify analytical risks.
4. @Data Analytics build a KPI framework for acquisition quality.
5. @Data Analytics turn this into a dashboard with channel and week filters.
6. @Data Analytics create a reproducible notebook that tests whether support intensity tracks activation.

How to experiment

1. Change one or more values in the CSV.
2. Run python .\build_lab.py.
3. Ask Data Analytics to explain what changed and whether the recommendation still holds.
4. Ask for a different view: KPI report, dashboard, validation report, notebook, or chart.
5. Replace the synthetic CSV with your own file once the workflow feels familiar.

Important limitation

This data is synthetic. The findings demonstrate analysis and artifact workflows; they do not describe a real product or company.