Generate a basic cleaning process for numerical datasets, including handling missing values and outliers.
Task: Outline a step-by-step process to clean a numerical dataset for preliminary analysis. Context: You have a dataset with numerical features, and you need to ensure its quality before any modeling. Steps to include: 1. Identify missing values. 2. Suggest basic imputation methods for missing numerical data (e.g., mean, median). 3. Identify potential outliers. 4. Suggest simple methods for handling outliers (e.g., capping, removal). Output Format: Provide a concise, numbered list of steps.
Define basic data integrity rules for a given dataset and outline how to validate them.
Formulate fundamental guidelines for consistent and accurate data entry during field research, suitable for basic studies.
Create a detailed checklist for validating and cleaning research data, covering completeness, accuracy, consistency, and uniqueness checks.
