The Brief
Naledi Khumalo runs an eighteen-person furniture workshop in Cape Town. She sells through three channels — retail partners, direct commissions, and online — and she has a sense of what's working, but not the numbers to back it up. Revenue is up, but between retail commissions, shipping costs, and the time each piece takes to build, the picture isn't clear enough to plan on.
Her bookkeeper put together a year of sales data and a guide to what the columns mean. Naledi wants someone to go through it and either confirm her instincts or show her where she's wrong.
Your Role
You're picking up this analysis. You have Claude Code, DuckDB, Jupyter, and the full analytics toolkit from setup. Your job is to direct AI through the work — loading the data, computing the numbers, building the charts, writing the findings — and make sure what comes back is actually right.
AI handles the computation. You handle the thinking. When AI computes "total revenue," it doesn't check whether that number matches what Naledi's business actually means by revenue. That's on you.
What's New
Setup gave you the tools and a single-prompt demo. This is the first time you're doing it for real — for a client who needs the answer.
Everything is provided: an analysis spec that tells you what to compute, metric definitions that tell you what the numbers mean, a data dictionary that documents every column, chart specs, a narrative template, and verification targets so you can check AI's output against known-correct values. Your focus is on the loop itself — understanding the sequence from data to insight to communication — not on figuring out what to do next.
The hard part is not the computation. It's noticing when AI's number looks right but isn't — because it included refunds the definition excludes, or aggregated before deduplicating, or generated a chart with a truncated y-axis that misrepresents the trend.
Tools
- Python 3.11+ (via Miniconda, "analytics" environment)
- DuckDB
- Jupyter Notebook
- pandas
- matplotlib / seaborn
- Claude Code
- Git / GitHub
Materials
You'll work with these provided files:
- Dataset — twelve months of furniture sales data (~850 rows)
- Data dictionary — column definitions, types, constraints, and what each value means
- Analysis spec — the four business questions, expected outputs, chart specifications, and verification targets
- Metric definitions — precise definitions for revenue, profit margin, and revenue per workshop-week
- Narrative template — executive summary, key findings, and recommendation structure