Data Preprocessing and Quality Assurance

CONTEXT
In the realm of data analysis, data cleaning and preprocessing are crucial steps to ensure the reliability and accuracy of data. Clean data leads to trustworthy insights and informed decision-making.

OBJECTIVE
The objective of this prompt is to guide users through the steps of data cleaning, including identifying and addressing issues such as missing values, duplicates, incorrect formatting, and inconsistencies. Additionally, it emphasizes the importance of maintaining data quality throughout the process.

FORMAT
The response should include detailed methods for cleaning and preprocessing data, examples of common issues and their solutions, and best practices for quality assurance in datasets.

EXAMPLES
1) Identifying and filling missing values in a dataset using mean/mode/median imputation techniques. 2) Removing duplicate entries based on unique identifiers. 3) Standardizing date formats across different datasets.