Practice finding duplicates, missing values, inconsistent labels, and other data issues with documented challenges for SQL, Excel, and Python.
Compare the available challenges by issue type and the relationships involved. Start with a small, clearly defined problem before combining several cleaning steps.
| Dataset | Documented problems | Dirty variant | Reference |
|---|---|---|---|
| Ecommerce Sales & Returns | Duplicates, inconsistent labels, date formats, and blank required values | Included | Unchanged generated release |
| SaaS Subscriptions & Churn | Duplicates, inconsistent labels, date formats, and blank required values | Included | Unchanged generated release |
| Marketing Campaigns & Conversions | Duplicates, inconsistent labels, date formats, and blank required values | Included | Unchanged generated release |
| Inventory & Supply Chain | Duplicates, inconsistent labels, date formats, and blank required values | Included | Unchanged generated release |
Each published synthetic cleaning challenge documents the same issue classes. Find the affected rows rather than relying on a row-by-row answer key.
Preserve the original file, identify the problem, and state the rule used to correct it. Check whether the change affects row counts, totals, or relationships. Your final result should explain both what changed and why.
A negative amount may represent a refund. Repeated values may belong to separate transactions. Missing information may be meaningful. Use the field definitions and challenge notes before replacing values or removing rows.
Source files represent the published data before your own analysis. A cleaning challenge is a separate practice version with intentional problems. Use its notes and reference data to understand what you are expected to investigate.
Go beyond spotting an error. Practice the transformations and checks that show a correction did what you intended.
