Data Scraping Encyclopedia>Data Scraping Noun> Data Sampling

Data Sampling | Web Scraping Tool | ScrapeStorm

2024-08-22 19:39:45

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Abstract：Data sampling is a method of selecting a portion of a large data set to infer and analyze the entire data. ScrapeStormFree Download

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Introduction

Data sampling is a method of selecting a portion of a large data set to infer and analyze the entire data. The goal is to reduce the computing resources required to analyze the entire data and perform the analysis efficiently. The sampled data must be representative of the original data set, and proper sampling allows you to accurately determine overall trends and characteristics.

Applicable Scene

When dealing with large amounts of data, using all the data for analysis can require a lot of computing time and resources. For example, data sampling is often used in big data analysis and machine learning model training. In order to get a comprehensive understanding of the data, we can first sample some data and perform a simple analysis. At this stage, important features and trends are identified and used for subsequent detailed analysis. In cases such as product quality inspection, where 100% inspection is not possible, sampling can be done and the overall quality can be estimated based on this sample.

Pros: Sampling can significantly reduce computation time and memory usage. This enables faster analysis. Sampling allows you to identify key trends and features in your data before analyzing the entire data. Sampling allows you to filter out unnecessary, redundant data in your analysis and focus on more important data.

Cons: Inadequate sampling may result in results that do not accurately reflect the entire data. There is a risk of drawing incorrect conclusions, especially if the sample is biased. Sampling reduces the sample size compared to the entire data set, which can reduce the accuracy of the analysis results. In particular, rare events or outliers may not be detected if the sample size is insufficient. If appropriate sampling techniques are not used, the sample may not be representative of the population. This may lead to misinterpretation of the analysis results.