Data normalization is a preprocessing technique in data analytics that adjusts values measured on different scales to a common scale, often between 0 and 1 or -1 and 1. This process is essential because, in raw datasets, features can have vastly different ranges. For instance, in a dataset predicting sales, "age" might range from 18 to 70, while "income" could range from thousands to hundreds of thousands. Without normalization, algorithms like K-Nearest Neighbors (KNN) and neural networks can place more weight on features with larger ranges, leading to biased results and reducing model accuracy.

Normalization helps ensure that all features contribute equally to the model, thus enhancing the accuracy and stability of predictions. By bringing data onto a similar scale, it allows the algorithm to converge faster and avoids skewed interpretations, particularly in distance-based algorithms. Moreover, it prevents large-value features from dominating small-value ones, enabling a balanced and accurate assessment of all features in the model.

For a deeper understanding of techniques like normalization and their practical applications in machine learning, consider enrolling in a data analyst certification course to develop essential skills and advance in the field.