Example Concept: Data aggregation involves summarizing detailed data into a more compact form, such as averaging, summing, or counting, to reduce dimensionality and highlight key trends. This technique is particularly useful in scenarios like time series analysis, where aggregating data by day, week, or month can reveal seasonal patterns and trends that are more predictive than raw, granular data. Aggregation can also improve computational efficiency by reducing the dataset size, making it more manageable for training models.