Description: Descriptive analytics is a statistical technique that focuses on the collection and presentation of historical data to identify trends and patterns. This type of analysis allows researchers and analysts to summarize and organize data in a way that meaningful conclusions can be drawn. Through measures such as mean, median, mode, and standard deviation, descriptive analytics provides a clear view of the characteristics of a dataset. Additionally, it relies on graphical visualizations, such as histograms and scatter plots, to facilitate understanding of the information. Its relevance lies in its ability to provide a solid foundation for informed decision-making, as it enables users to identify behaviors and trends that may be crucial for developing future strategies. In the context of explainable artificial intelligence, descriptive analytics also plays an important role by helping models to be more transparent and understandable, providing clear context about the data used in training algorithms. In summary, descriptive analytics is a fundamental tool in applied statistics that allows analysts to better understand data and effectively communicate their findings.
History: Descriptive analytics has its roots in statistics, which was formalized as a discipline in the 18th century. As data collection became more systematic, especially with the rise of industrialization and census-taking, methods were developed to summarize and present this data. In the 20th century, with the advancement of computing, descriptive analytics became more accessible and was integrated into various disciplines, from economics to psychology. The popularization of statistical software in the 1980s and 1990s further facilitated its use in research and industry.
Uses: Descriptive analytics is used in a variety of fields, including market research, public health, education, and social science. It allows analysts to summarize large volumes of data, identify trends and patterns, and effectively communicate findings. In the business realm, it is used to assess the performance of products and services, while in public health, it helps identify disease outbreaks and trends in population health.
Examples: An example of descriptive analytics is the use of sales statistics to identify trends in consumer behavior over time. Another example is the analysis of demographic data in public health studies to understand the distribution of diseases across different population groups. In the educational realm, student grades can be analyzed to identify performance patterns and areas that require attention.
