🧹 Interactive Data Cleaning Simulator
Practice identifying and correcting common data quality issues in a realistic M&E dataset
📋 Your Challenge: Youth Skills Training Program Evaluation
Context: You've received survey data from 25 participants in a vocational skills training program. The data was collected by multiple enumerators using paper forms and then digitized.
Your Task: Clean this messy dataset by identifying and correcting missing values, format inconsistencies, duplicates, outliers, and logical errors before analysis can begin.
Variables: Participant ID, Age, Gender, Training Completion Date, Pre-Test Score, Post-Test Score, Employment Status, Monthly Income
0/20
Errors Fixed
0%
Accuracy Rate
0%
Data Quality Score
Stage 1: Missing Data
Identify and handle missing values
Stage 2: Format Issues
Standardize inconsistent formats
Stage 3: Validation
Fix outliers and logical errors
Raw Dataset
Missing Data
Format Error
Outlier
Inconsistent
Fixed
🎯 Data Cleaning Results
0%
Overall Data Quality Score
