What are the best practices for cleaning and preprocessing data?
The question is about Data Engineering .
The best ways to clean and prepare data are to remove duplicates, handle missing data, and standardise formats. Scale or normalise numbers, encode categories, and identify outliers that could distort results. Automating these steps reduces errors and saves time, ensuring the data is accurate before analysis begins.
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Vladyslav is a Senior Data Engineer focused on building data pipelines and infrastructure for AI-driven products. Started as a Python developer, he moved deeper into data engineering through hands-on work with ETL/ELT systems... Read More
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