Redacción Tokio | 31/01/2023
Data has always been there and there has always been a tendency to want to analyze it in order to make the best decisions, whether from a business or government perspective. Over time, the amount of data we’ve generated has increased considerably, so data sampling, that is, the division of information into smaller data subsets, has become increasingly important.
Very briefly, data sampling can be defined as the different statistical analysis techniques used to select, manipulate and analyze a subset of representative data in order to obtain relevant information.
However, there are many other aspects involved in the concept of data sampling. In this article, we’ll go through not only what this model is or how it works, but also how it relates to data analysis and the importance of accessing quality Big Data training. Let’s go!
What is data sampling?
As we’ve mentioned above, data sampling encompasses all statistical techniques that deal with creating subsets of data from large samples of information. This allows data scientists, big data analysts, and other related professionals to identify patterns and trends within a larger data set, but building up from smaller samples.
Thanks to data sampling, data analysis professionals are able to work with a smaller amount of information that is much more manageable. As such, they can build statistical samples on which to run analytical models more quickly.
Identifying and analyzing a representative sample is more efficient and cost-effective than analyzing all data or the whole sample population.
Data sampling can be especially useful with data sets that are too large to be efficiently analyzed in full. Therefore, they represent a very valuable set of techniques in the context of Big Data or Data Science.
Despite this, the possibility of generating errors must be taken into account, which depends on the size of the sample that is being used. That is, creating smaller subsets of data can reveal important information about the larger set, although analyzing large samples sometimes produces more accurate results by making the data more difficult to manipulate and misinterpret.
How does data sampling work?
Data sampling encompasses different techniques and methods, so that picking the right one depends on each specific data set and the situation. Sampling can be based on probability, in which case, random numbers that correspond to given points within the data set are used. This method ensures that there is no correlation between the chosen points for the sample.
On the other hand, non-probabilistic sampling techniques are also available. In this case, the approach is based on the analyst’s judgment, who decides on the sample of data. In this case, it is more difficult to extrapolate whether the sample is representative or has an influence on the starting data set.
Data samples can be put together following different techniques both based on probability or the specific criteria that has been predefined by analysts and researchers.
Once generated, a sample can be used for predictive analysis. For example, a retail company could use data sampling to discover patterns in customer behavior and then use predictive modeling to create the most effective sales strategies.
Types of data sampling: probabilistic and non-probabilistic
As we’ve said above, there are two types of data sampling, one with a probabilistic approach and the other with a non-probabilistic approach:
Probabilistic data sampling
There’re different types of approaches within this framework:
- Simple random sampling. In this case, software is used to randomly select subjects or specific points within the entire population of the statistical sample.
- Stratified. In stratified sampling, data sets are created based on a common interest factor for what the analyst needs. From that factor or common point, data is selected randomly for each subgroup or subset of data.
- Cluster sampling. The larger data set is divided into smaller subsets called clusters. Once this division is made, a random sampling is carried out among them.
- Multistage. This is a more complex way of performing cluster data sampling. This method also divides the reference data set into multiple clusters. However, these clusters are broken down based on secondary factors and are sampled and analyzed from there.
- Systematic. The sample is created by setting an interval that allows for the extraction of data from the largest set.
Non-probability data sampling
The following are within the non-probability data sampling efforts:
- Convenience sampling. Data is collected from a pool that is easily accessible and available for the analysis at hand
- Intentional. The analyst selects the data to be sampled based on predefined criteria that have been established either by him or by the company he works for.
- Consecutive. All the data of interest on each subject that meets certain criteria is collected until the desired sample size is covered.
- Quota sampling. The analyst guarantees the same representation for all subgroups in the data sample that is generated.
Would you like to become an expert in data analysis?
Statistical techniques for traditional data analysis rapidly become obsolete or are integrated into new trends and technologies. Data sampling, as we have seen, can be useful within the massive analysis of Big Data, but, in order to master it, it is necessary to access high quality training and education.
Keep in mind that Big Data analysis is becoming a very important field for different sectors. This makes qualified and well-trained professionals especially important. Therefore, if you want to work in data analysis, you must get ready in the most exhaustive manner.
At Tokyo School we are specialists in offering training related to new technologies. Big Data is no exception, which is the reason we also offer training courses related to this discipline.
Want to know more? Get in touch with us and get answers to all your questions! We can’t wait to meet you!