Defining data cleaning

Data cleaning and preparation is the methodical and strategic process of identifying, rectifying, and mitigating inaccuracies, inconsistencies, and imperfections in your dataset. It is the essential step that bridges the gap between raw data and meaningful insights. Just as a skilled artisan refines raw materials to create a masterpiece, data cleaning transforms your dataset into a polished and reliable foundation for analysis.

Recognizing the inevitability of data imperfections, the task at hand is to establish a framework and adhere to principles that guide your data cleaning efforts. This framework is crucial for preventing the cycle of perpetual data cleaning, analysis, and the subsequent return to data cleaning due to oversights in the initial iteration. Without a structured approach, the process becomes cyclical and may lead to inefficiencies, compromising the effectiveness of your analyses.

In the following section, you will begin to learn about...