Data cleaning, also known as data cleansing or data scrubbing, is the process of fixing or removing incorrect, incomplete, or duplicate data. It's a fundamental step in data analytics because the quality of the data affects the accuracy of the insights and decisions made. What's involved in data cleaning? Identifying errors: Find errors or corruptions in the data Correcting errors: Fix errors like misspellings, incorrect word usage, or capitalization mistakes Deleting errors: Remove data that's inaccurate, incomplete, or duplicate Preventing errors: Manually process data to prevent the same errors from happening again Why is data cleaning important? It ensures that the data is correct, consistent, and usable It helps to make sure that the data is reliable and accurate It helps to ensure that the insights and decisions made are accurate and reliable What tools can be used for data cleaning? Data Cleaner app: Allows users to process and clean raw data OpenRefine: An open-source data tool that allows users to transform data between different formats Excel: Allows users to remove duplicate values