Data Cleaning

Data cleaning, which is often referred as data cleansing or data scrubbing, is one of the most important and initial steps in the data analysis process. As a data analyst, the bulk of your work often revolves around understanding, cleaning, and standardizing raw data before analysis. Data cleaning involves identifying, correcting or removing any errors or inconsistencies in datasets in order to improve their quality. The process is crucial because it directly determines the accuracy of the insights you generate - garbage in, garbage out. Even the most sophisticated models and visualizations would not be of much use if they're based on dirty data. Therefore, mastering data cleaning techniques is essential for any data analyst.

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