**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.**

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