Data Sampling
Data sampling is a statistical technique used to select a subset of data points from a larger dataset, known as the population, to make inferences or analyses more manageable. It involves methods like random sampling, stratified sampling, or systematic sampling to ensure the sample is representative of the whole. This approach is crucial in data science, machine learning, and research to reduce computational costs, speed up processing, and handle large datasets efficiently.
Developers should learn data sampling when working with big data, machine learning models, or statistical analyses to avoid overfitting, reduce training times, and manage memory constraints. It is essential in scenarios like A/B testing, data preprocessing for model training, and exploratory data analysis where full datasets are impractical. For example, in building recommendation systems, sampling helps test algorithms on smaller datasets before scaling up.