Data Transformation and Cleansing focuses on converting raw data into a clean, consistent, and usable format for analytics and machine learning. Data cleansing involves identifying and correcting errors, handling missing values, removing duplicates, and fixing inconsistencies. Data transformation restructures data by normalizing formats, encoding categorical variables, scaling features, and aggregating information for better analysis. This training explains how these processes improve data quality and ensure accurate insights from datasets. It also covers common techniques used in ETL pipelines and data preprocessing workflows. You will learn how to prepare high-quality datasets that support reliable reporting, predictive modeling, and business intelligence applications. The course emphasizes practical methods for improving data integrity and usability in real-world systems.
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