Like ingredients for a recipe, your data needs to be prepared for use too!
Data preparation is the process of transforming raw data into a clean, structured, and analytically ready format. It involves a series of steps such as data cleaning, integration, transformation, and enrichment, with the aim of ensuring data quality and usability for analysis and decision-making.
During data preparation, raw data is carefully examined and cleansed to remove inconsistencies, errors, duplicates, and missing values. Integration involves combining data from different sources into a unified format, enabling a comprehensive view of the information. Transformation techniques like normalization, aggregation, and feature engineering are applied to reshape and manipulate the data to fit specific analytical requirements. Additionally, data enrichment may involve appending additional data or deriving new variables to enhance the dataset’s context and value.
How is data prepared?
Collect data – assembling data needed for ML
Clean data – corrects errors and fills gaps in missing data
Label data – identifying raw data and adding more labels
Validate and visualize – ML teams can explore data to make visualizations (ex. Histograms, scatter plots)
Data preparation is crucial in the field of data science as it lays the foundation for accurate and reliable analysis. It helps in reducing bias, improving data quality, and enabling the discovery of meaningful patterns and insights.