In data science, there are various sampling techniques used to select a subset of data points from a larger dataset for analysis.
Sampling techniques are used in data science to collect data from a subset of a population to make inferences about the entire population. Here are some of the common sampling techniques:
Simple Random Sampling: Every individual in the dataset has an equal chance of being selected. This method is unbiased but might not be efficient for large datasets.Stratified Sampling: The population is divided into distinct subgroups (strata) based on certain characteristics, and then random samples are taken from each subgroup. This ensures representation from each stratum.Systematic Sampling: Selecting data points at regular intervals from an ordered list. For instance, every 5th or 10th item is chosen after randomly selecting the starting point.Cluster Sampling: Dividing the population into clusters, then randomly selecting some clusters and using all individuals within those selected clusters for the sample.Probability Proportional to Size Sampling: Larger samples have a higher probability of being selected. This method is useful when the data isn't uniformly distributed.Stratified Random Sampling: Similar to stratified sampling but uses random sampling within each stratum instead of selecting the entire stratum.Adaptive Sampling: Sampling based on the information gained during the sampling process. This method adjusts the sampling strategy as more data is collected.The choice of sampling technique depends on the specific characteristics of the dataset, the research question, computational resources, and the desired level of accuracy or representation in the analysis.