IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Data normalization is a critical preprocessing step in machine learning and data science that enhances the performance and efficiency of various algorithms.

One advantage is the stabilization of model weight updates. Normalization helps ensure that all input features contribute equally to the learning process, which prevents certain weights from being dominated by features with larger ranges. This leads to more stable updates during the optimization process, which is particularly important for gradient-based methods.

Another benefit is that normalization can significantly speed up the training time for neural networks. When the data is normalized, the inputs to the network are scaled to a similar range, which facilitates faster convergence of the optimization algorithms. This means that the learning model can reach optimal weights more quickly compared to unnormalized data, where variations in scale might lead to slow and inefficient training.

Additionally, while normalization might not directly highlight outliers in the same way that certain other techniques do, it can still impact the observation and interpretation of those outliers. By bringing all features to a common scale, it is easier to identify data points that deviate significantly from the normalized mean, thus indirectly assisting in outlier detection.

Because of these advantages, all the aspects mentioned contribute positively to the effectiveness of data normalization in machine learning workflows, leading to the conclusion that all listed