Effective methods of data visualization and statistical analysis

Abstract

Data visualization is an important step in the information analysis process, which allows you to quickly identify patterns, anomalies, and trends in datasets. The purpose of this work is to consider the most effective visualization methods and evaluate their applicability from the point of view of statistical analysis. The paper analyzes tools such as histograms, scatter plots, heat maps, boxplot graphs, and line graphs. Their effectiveness when working with different types of data is evaluated. Visualization software tools are also discussed, including the Python libraries Matplotlib, Seaborn, and Plotly.