What are the best practices for cleaning and preprocessing data?

The best practices regarding the removal of duplicates, handling missing data, and normalization or standardization of all data are used in a common format.

Data Analysts need to scale or normalize numerical data, encode categorical variables, and detect influential outliers distorting their findings. In this respect, it will be guaranteed that the data at hand is appropriate and consistent at the forefront of entry to analysis, since this directly impacts the insights generated. Where possible, these tasks might be automated to save time and reduce human error.