An Overview of Descriptive Analytics and Data Visualization

Descriptive analytics, a fundamental component of data science, focuses on analyzing historical data to identify patterns and trends. By employing statistical methods and data visualization techniques, descriptive analytics provides valuable insights into past events and facilitates informed decision-making. This research explores the key concepts and techniques associated with descriptive analytics. It highlights the importance of data visualization in understanding complex datasets and introduces various visualization methods, including dashboards, scatter plots, bar charts, histograms, box plots, pair plots, and heat maps. Additionally, the study discusses the use of statistical metrics for analyzing numerical data and understanding its center and spread. By learning descriptive analytics, researchers and data scientists can extract meaningful information from structured data and gain a deeper understanding of the underlying trends and patterns. This knowledge can be applied to various domains, including business intelligence, research, and decision-making.