What is the most efficient way to normalize data?

In data science, normalization usually refers to adjusting values measured on different scales to a common scale. The most efficient method often depends on the specific context of the data. For instance, using Min-Max scaling (rescaling data to a range of [0, 1]) is simple and effective for data bounded within a known range. However, in cases where data has outliers or is not normally distributed, Z-score normalization (standardizing data to have a mean of 0 and a standard deviation of 1) is often more appropriate. The key is to balance the need for a standardized scale with the inherent characteristics of the data.

Normalization principles can meaning different when it face in different stage of data pipeline: data modeling, data mapping and data consuming!

Let me summarize the methods of data normalization into some keywords categorized by its objective. 1. Identification: - Primary Key - Null Values - Duplicates 2. Handling Partial Dependencies: - Splitting Tables - Creating Foreign Keys 3. Addressing Transitive Dependencies: - Creating Separate Tables - Linking with Foreign Keys 4. Dealing with Multivalued Dependencies: - Separate Tables - Foreign Keys 5. Managing Join Dependencies: - Decomposition - Natural Grouping - Foreign Keys 6. Considerations for Denormalization: - Redundant or Derived Data - Performance Improvement - Caution due to Inconsistency or Duplication

In data science, normalization is crucial for ensuring consistency and comparability of data. It improves model performance by scaling features to a similar range, which is particularly beneficial in algorithms that rely on distance calculations, like K-means clustering or k-NN. For instance, in a dataset with income and age as features, without normalization, the higher range of income would dominate the model's outcome. By normalizing, both income and age contribute equally to the analysis. However, the choice of normalization method should consider the data distribution and the specific algorithm used. For algorithms insensitive to feature scaling, like decision trees, normalization might not be necessary.

Normalization will help you avoid data redundancy and thus, storage constraints. It will also help enforce integrity as well as optimize a Database for write operations. But in an Analytical space, read operations are a priority and hence, denormalization is often adopted to enable quicker querying and lesser joins.

In my experiences; the all benefits are related with maintenance and usability of field in data modeling. ?? Maintan one copy of value, can help in topics like tracking of historical records, effectivily use of storage, apply ABAC easier, fast propagation of any update value. ?? Usability depend how structure the data in most granularity level from raw data to use; This point give us the trade-off topic with performance and data quality.

Normalization is a crucial step in machine learning that involves scaling data to a standard range or distribution. Various techniques can be used for normalization, including Min-Max scaling (which scales data to a range between 0 and 1), Z-Score standardization (which transforms data to have a mean of 0 and standard deviation of 1), Robust Scaling (which is robust to outliers), and Log Transformation (useful for skewed data). The choice of normalization technique depends on the characteristics of the data and the requirements of the machine learning algorithm being used.