🧹Data Cleaning and Preprocessing

In the world of data hacking, one of the most critical steps is data cleaning and preprocessing. This process involves removing or correcting errors in the data, handling missing values, standardizing data formats, and transforming variables for analysis. By cleaning and preprocessing data effectively, you can ensure the accuracy and reliability of your analysis results.

These techniques allow data analysts a way to identify and handle missing data, outliers, and errors in the datasets. Much of this can be done with spreadsheets like Microsoft Excel, but sometimes involve programming languages like Python and more sophisticated tools.

Remember that the data that lives in these computer systems most often represents the real world. If there is data that isn't accurate, the questions we ask to our data systems won't give us correct results about the real world.