Data Scaling vs Data Normalization

Data scaling and normalization are two terms that are often used interchangeably, but they actually refer to slightly different concepts.

Data scaling refers to transforming a dataset's values so that they fall within a specific range. This can involve rescaling the data to a specific minimum and maximum value, or standardizing the data so that it has a mean of zero and a standard deviation of one. The goal of data scaling is to ensure that all the dataset's features are on the same scale so that no feature dominates the others.

Normalization, on the other hand, refers to the process of transforming the values of a dataset so that they conform to a specific distribution. This can involve transforming the data so that it has a normal (Gaussian) distribution or some other distribution. Normalization aims to make the data more interpretable or to meet the assumptions of a particular statistical test or machine learning algorithm.

Data scaling is a more common preprocessing step in machine learning, as it is often necessary to ensure that all features are on the same scale to avoid bias and improve accuracy. Normalization is less commonly used but can be important in certain situations, such as when working with data with a skewed distribution or when using certain statistical tests.