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Transform data to a common unit or format to facilitate comparison and analysis.

Data standardization definition:

Data standardization is the process of transforming data into a common format that allows for easy comparison and analysis. This process is useful when working with data that is collected from different sources or has different units of measurement. Data standardization involves scaling the data to a common range, usually with a mean of 0 and standard deviation of 1.

Data standardization example using Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

In Python, we can use the StandardScaler class from the sklearn.preprocessing module to perform data standardization. Here's an example:

import numpy as np
from sklearn.preprocessing import StandardScaler

# create a sample dataset
data = np.array([[1, 2], [3, 4], [5, 6]])

# create a StandardScaler object
scaler = StandardScaler()

# fit and transform the data
scaled_data = scaler.fit_transform(data)


This code will generate the following output:

[[-1.22474487 -1.22474487]
 [ 0.          0.        ]
 [ 1.22474487  1.22474487]]

In this example, we create a sample dataset with 3 rows and 2 columns. We then create a StandardScaler object and fit it to the data. Finally, we transform the data using the fit_transform method and print the scaled data. The output shows that each column has been scaled to have a mean of 0 and a standard deviation of 1.

Other data engineering terms related to
Data Transformation:


Aligning data can mean one of three things: aligning datasets, meeting business rules or arranging data elements in memory.

Big Data Processing

Process large volumes of data in parallel and distributed computing environments to improve performance.

Clean or Cleanse

Remove invalid or inconsistent data values, such as empty fields or outliers.


Group data points based on similarities or patterns to facilitate analysis and modeling.


Remove noise or artifacts from data to improve its accuracy and quality.


Optimize data for faster read access by reducing the number of joins needed to retrieve related data.


Transform continuous data into discrete categories or bins to simplify analysis.


Extract, transform, and load data between different systems.


Extract a subset of data based on specific criteria or conditions.


Convert data into a linear format for efficient storage and processing.


Fill in missing data values with estimated or imputed values to facilitate analysis.


See 'wrangle'.


Standardize data values to facilitate comparison and analysis. organize data into a consistent format.


Convert a large set of data into a smaller, more manageable form without significant loss of information.


Change the structure of data to better fit specific analysis or modeling requirements.


Convert data into a linear format for efficient storage and processing.


Break down large datasets into smaller, more manageable pieces for easier processing and analysis.


An imbalance in the distribution or representation of data.


Convert data into tokens or smaller units to simplify analysis or processing.


Convert data from one format or structure to another.


Convert unstructured data into a structured format.