Data Reduction
Data reduction is a technique in data processing and analysis that involves reducing the volume of data while preserving its essential information and patterns. It is commonly used in fields like data mining, machine learning, and big data analytics to improve efficiency, reduce storage costs, and enhance computational performance. Methods include dimensionality reduction, sampling, aggregation, and compression.
Developers should learn data reduction when working with large datasets, such as in big data applications, machine learning model training, or real-time analytics, to handle scalability and performance challenges. It is crucial for reducing memory usage, speeding up algorithms, and making data more manageable without significant loss of accuracy, especially in resource-constrained environments like edge computing or mobile apps.