Industry surveys of data professionals consistently find that cleaning and preparing data takes up the largest share of a typical analytics project, often more time than the actual analysis. This module teaches you to handle missing values thoughtfully: sometimes you drop the row, sometimes you fill it with a sensible estimate (like the column's median), and sometimes a missing value is itself meaningful information. You will also learn to catch duplicate records, standardize inconsistent entries like "Lagos," "lagos," and "LAGOS" into one format, and validate that values fall within expected ranges, for example that a reported age is not 250 years. Finally, you will practice documenting every cleaning decision so another analyst, or you in six months, can understand and reproduce your work.