Data Quality Assurance through Preprocessing and Sanitization
A prompt focusing on improving data quality by identifying and fixing issues in datasets.
11 month ago
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CONTEXT
Data cleaning is a crucial step in data analysis that ensures the accuracy and reliability of the data being used. It involves removing errors, inconsistencies, and irrelevant information from the dataset.
OBJECTIVE
The objective of this prompt is to guide users through the process of preprocessing and sanitizing their datasets to maintain high quality and integrity of the data, allowing for accurate analyses and insights.
FORMAT
Users will be provided with a structured approach to cleaning their data, including steps for identifying missing values, correcting inconsistencies, and ensuring data types align correctly. Suggested tools and methods for each step will be included.
EXAMPLES
1. Detect and fill missing values in a dataset. 2. Remove duplicate entries from a customer database. 3. Normalize data formats in a sales report to ensure consistency.
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