Data normalization is a crucial step in the data preprocessing phase, especially when preparing data for statistical analysis or machine learning models. Its main purpose is to standardize the range and distribution of numerical features in a dataset, so that no single feature dominates the learning process due to scale differences. This becomes particularly important when features have different units or scales, such as age (in years) and income (in lakhs).

There are several types of normalization techniques, including min-max scaling, z-score normalization, and decimal scaling. Min-max scaling transforms the data to a fixed range (usually 0 to 1), while z-score normalization adjusts the data based on the mean and standard deviation, centering the data around zero.

Without normalization, algorithms like k-nearest neighbors, support vector machines, and gradient descent-based models may produce biased results or take longer to converge. Normalization ensures that each feature contributes equally, enhancing model accuracy and speed.

In summary, data normalization improves the performance, efficiency, and reliability of data analysis and machine learning tasks, making it a foundational skill for aspiring analysts. To build such skills, explore a data analyst course in Noida with placement.