# Data science recipe steps
<a name="recipe-actions.data-science"></a>

Use these recipe steps to tabulate and summarize data from different perspectives, or to perform advanced transformations.

**Topics**
+ [BINARIZATION](recipe-actions.BINARIZATION.md)
+ [BUCKETIZATION](recipe-actions.BUCKETIZATION.md)
+ [CATEGORICAL\$1MAPPING](recipe-actions.CATEGORICAL_MAPPING.md)
+ [ONE\$1HOT\$1ENCODING](recipe-actions.ONE_HOT_ENCODING.md)
+ [SCALE](#recipe-actions.SCALE)
+ [SKEWNESS](recipe-actions.SKEWNESS.md)
+ [TOKENIZATION](recipe-actions.TOKENIZATION.md)

## SCALE
<a name="recipe-actions.SCALE"></a>

Scales or normalizes the range of data in a numeric column.

**Parameters**
+ `sourceColumn` – The name of an existing column.
+ `strategy` – The operation to be applied to the column values:
  + `MIN_MAX` – Rescales the values into a range of [0,1]
  + `SCALE_BETWEEN` – Rescales the values into a range of 2 specified values.
  +  `MEAN_NORMALIZATION` – Rescales the data to have a mean (μ) of 0 and standard deviation (σ) of 1 within a range of [-1, 1]
  +  `Z_SCORE` – Linearly scale data values to have a mean (μ) of 0 and standard deviation (σ) of 1. Best for handling outliers.
+ `targetColumn` – The name of a column to contain the results.

**Example**  
  

```
{
    "Action": {
        "Operation": "NORMALIZATION",
        "Parameters": {
            "sourceColumn": "all_votes",
            "strategy": "MIN_MAX",
            "targetColumn": "all_votes_normalized"
        }
    }
}
```