Summary

In the first half of this chapter, we established the critical role of data visualization and storytelling in the field of data science. Beginning with an overview of why data visualization is crucial, we delved into a framework for choosing the right visualization based on data types and the goal of communication. We explored a variety of data visualization types, such as bar charts, pie charts, histograms, scatter plots, and box plots, discussing their use cases, creation processes, and tips for enhancing their storytelling power. Additionally, we analyzed various visualization tools, including Power BI, Tableau, R’s Shiny, Python’s Matplotlib, and Seaborn, providing insights into their advantages, limitations, and ideal use cases.

The latter part of this chapter focused on the practical aspects of data visualization and storytelling. We covered the best practices for creating effective dashboards, reports, and KPIs, emphasizing clean, uncluttered visuals...