Data Transformation and Cleansing Strategies focus on preparing raw data for analysis by improving its quality, consistency, and structure. Data cleansing involves identifying and fixing errors such as missing values, duplicates, and inconsistencies. Data transformation converts data into usable formats through normalization, encoding, aggregation, and scaling techniques. This training explains how to design effective strategies for improving data quality across different systems and pipelines. It also covers common challenges in data preparation and methods to handle large, complex datasets efficiently. You will learn how to apply structured approaches to ensure reliable, accurate, and analytics-ready data. The course also highlights best practices for building robust data preparation workflows in real-world data engineering and analytics environments.
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