The Data Engineering toolset contains tools for preparing fields for analysis workflows, such as transforming, standardizing, encoding, and reclassifying values. These tools are designed to ensure that datasets are clean, consistent, and ready for modeling or analysis workflows.
| Tool | Description |
|---|---|
Converts categorical values (string, integer, or date) into multiple numerical fields, each representing a category. The encoded numerical fields can be used in most data science and statistical workflows including regression models. | |
Creates a table of descriptive statistics for one or more input fields in a table or feature class. | |
Reclassifies values in a numerical or text field into classes based on bounds defined manually or using a reclassification method. | |
Standardizes values in fields by converting them to values that follow a specified scale. Standardization methods include z-score, minimum-maximum, absolute maximum, and robust standardization. | |
Transforms continuous values in one or more fields by applying mathematical functions to each value and changing the shape of the distribution. The transformation methods in the tool include log, square root, Box-Cox, multiplicative inverse, square, exponential, and inverse Box-Cox. |