Scientific Data Preprocessing

Data preprocessing

Data preprocessing skill. Missing value imputation, outlier detection, normalization, feature scaling, encoding, and data cleaning pipelines for scientific datasets.

Use This Skill When

• Missing value imputation.
• Outlier detection.
• Normalization.
• Feature scaling.
• Encoding.

Required Inputs

• Research objective, decision target, or hypothesis.
• Available data, source constraints, and domain assumptions.
• Required outputs, success metrics, and deadline or reproducibility constraints.

Workflow

1. Confirm scope, assumptions, and the exact artifact set to save.
2. Apply the narrowest domain method that answers the request with defensible evidence.
3. Save code, tables, figures, and intermediate outputs to files instead of chat-only output.