Data Cleaning Challenge
Clean a deliberately messy employee master and prepare it for reliable reporting.
Beginner · 25–40 min
Objectives
- Detect dirty text
- Clean spaces
- Standardize labels
- Find duplicates
Project dataset
| Row | A: Employee | B: City | C: Status | D: Employee ID |
|---|---|---|---|---|
| 2 | Amit | mumbai | Active | EMP001 |
| 3 | Priya | PUNE | active | EMP002 |
| 4 | Amit | Mumbai | Active | EMP001 |
| 5 | neha | Mumbai | ACTIVE | EMP003 |
| 6 | Ravi | nashik | Closed | EMP004 |
Guided checkpoints
Inspect the raw data
Look for leading/trailing spaces, inconsistent case and repeated IDs.
Identify data-quality problems before reporting.
Clean spaces
Use TRIM/CLEAN on imported employee or city text where needed.
Remove unwanted spaces and non-printing characters.
Standardize labels
Standardize city and status values so equivalent entries match exactly.
Create consistent report categories.
Check duplicates
Use duplicate checking or a COUNTIF-based check to locate repeated Employee IDs.
Prevent duplicate master records.
Download sample project .xlsx · All portfolio projects
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