Advanced Methods For Visualization And Interpretation Data With Python

Abstract

The article explores the essential steps and key techniques of exploratory data analysis, starting with the preparation and structuring of information, followed by the calculation of fundamental statistical metrics. Using the Iris dataset as an example, correlations are analyzed and various visualization techniques are employed, from basic graphs such as histograms and scatter plots, to advanced visualizations, including combined and 3D graphs. In addition, the concept of clustering is introduced as a crucial tool to detect hidden patterns in data. Throughout the process, practical recommendations are presented for selecting and applying the most appropriate methodologies, thus allowing for more accurate and useful interpretations. This approach seeks not only to facilitate the understanding of the data, but also to maximize its analytical value through the effective use of statistical and visual tools.