Data Cleaning Skill

Overview

Master data cleaning and preprocessing techniques essential for reliable analytics.

Topics Covered

• Missing value handling (imputation, deletion)
• Outlier detection and treatment
• Data type conversion and validation
• Duplicate identification and removal
• String cleaning and normalization

Learning Outcomes

• Clean messy datasets
• Handle missing data appropriately
• Detect and treat outliers
• Ensure data quality

Error Handling

| Error Type | Cause | Recovery | |------------|-------|----------| | Memory error | Dataset too large | Use chunking or sampling | | Type conversion failed | Invalid data format | Apply preprocessing first | | Encoding issues | Wrong character encoding | Detect and specify encoding | | Validation failure | Data doesn't meet schema | Review and adjust validation rules |

Related Skills

• programming (for automation)
• foundations (for data quality concepts)
• databases-sql (for SQL-based cleaning)