👉 Cleaning math involves understanding and applying the principles of data cleaning to ensure datasets are accurate, consistent, and reliable. It starts with identifying and handling missing values, where techniques like imputation or removal are applied based on the data context. Outliers are detected and managed using statistical methods or domain knowledge to avoid skewing analysis results. Data normalization and standardization ensure uniformity across different scales, while deduplication removes redundant entries to maintain data integrity. Consistency checks verify that data formats and values align with expected patterns, and text cleaning processes like tokenization and stemming prepare textual data for analysis. Each step is crucial to transform raw, often messy data into a clean, usable format that supports accurate statistical modeling and analysis.
