Description
Turn messy data into a clean, validated, analysis-ready dataset
The Data Cleaning and Validation Practical guides you through a complete data-quality workflow using Python.
You will work with a realistic synthetic service-delivery dataset containing common quality issues, including missing values, duplicate records, inconsistent categories, invalid dates, incorrect identifiers, and values outside acceptable ranges.
Rather than silently correcting problems, you will define clear validation rules, document your cleaning decisions, rerun quality checks, and produce an auditable set of outputs.
What you will practice
- Inspect dataset structure, fields, and data types
- Assess and handle missing values
- Detect duplicate records
- Standardize text and categorical values
- Validate dates and identifiers
- Check numeric values against acceptable ranges
- Apply clear and defensible cleaning rules
- Rerun quality checks after cleaning
- Document data-quality decisions
- Export clean data and supporting quality-control results
What you will produce
- A cleaned, analysis-ready CSV dataset
- A data-quality summary
- A documented cleaning log
- Quality-control files for missing values, duplicates, invalid dates, identifiers, and ranges
- Charts summarizing the identified data-quality issues
- Portfolio-ready evidence of a reproducible cleaning workflow
Who this practical is for
This practical is designed for students, early-career data professionals, researchers, monitoring and evaluation practitioners, and anyone responsible for preparing tabular data for analysis or reporting.
Basic familiarity with Python and tabular data is helpful. Advanced programming experience is not required.
The supplied dataset is synthetic and intended only for teaching and workflow testing. No real participant records are used.
