What data work really is
From messy files to clear answers
Data work usually follows the same steps:
- Ask a question — "Which items earn the most?" "When are we busiest?"
- Get the data — a CSV export, a spreadsheet, a database
- Clean it — fix typos, missing values, duplicates and formats
- Analyse it — totals, averages, groups, trends
- Show it — a chart or table someone can understand in seconds
- Explain it — what it means and what to do next
Data professionals often say cleaning takes most of the time. This course gives it a whole module, because that's what real work is like.
Meet the dataset
Corner Café has exported a year of sales from its till: cafe_sales_2025.csv. Each row is one item on an order:
| Column | Example |
|---|---|
| order_id | 10001 |
| date | 01/01/2025 (Australian day/month/year) |
| time | 13:50 |
| item / category | Chai latte / Tea |
| quantity | 1 |
| unit_price | $5.20 |
| payment | Card |
| staff | Jordan |
Like real till exports, it has problems hiding in it. You'll find and fix every one. (The café and its data are made up for this course, but every problem in it is a common real-world one.)
Why Python and not just a spreadsheet?
- Repeatable: run the same cleaning and report every week in seconds
- Bigger data: millions of rows are no problem
- Traceable: every step is written down, so anyone can check it
Try it
Download the starter files and unzip them. Open data/cafe_sales_2025.csv in Excel or Google Sheets and scroll through it. Can you spot any problems already?
Downloads
1 file to download once you enrol.