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name: data-cleaning description: Data cleaning, preprocessing, and quality assurance techniques version: "2.0.0" sasmp_version: "2.0.0" bonded_agent: 05-programming-expert bond_type: SECONDARY_BOND

Skill Configuration

config: atomic: true retry_enabled: true max_retries: 3 backoff_strategy: exponential

Parameter Validation

parameters: tool_preference: type: string required: true enum: [python, r, excel, sql] default: python data_size: type: string required: false enum: [small, medium, large] default: medium

Observability

observability: logging_level: info metrics: [rows_cleaned, missing_handled, duplicates_removed]

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

Related Skills

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