Introduction

Data visualization is the process of transforming raw data into visual formats such as charts, graphs, and maps to make it easier to understand, interpret, and communicate. Data visualization serves as a bridge between complex datasets and actionable insights, allowing analysts, decision-makers, and audiences to identify patterns, trends, and anomalies at a glance. Visualization is integral to both exploring data—when you’re uncovering relationships and insights—and explaining data, where the focus shifts to effectively communicating those findings to others. Whether used in business, science, or education, well-designed visualizations simplify complexity, making data accessible and actionable.

We will learn about exploring data through visualization and explaining data through visualization. Data exploration involves using techniques like scatter plots, histograms, and heatmaps to uncover hidden trends, correlations, or outliers. These exploratory visualizations are crucial during the initial stages of analysis, helping you understand the structure and nuances of the dataset. By contrast, explanatory visualizations focus on telling a story, emphasizing key takeaways for an audience. For example, a line chart might demonstrate the steady growth of sales over time, while a pie chart could show the distribution of market share among competitors. This dual purpose of visualization—exploration and explanation—enables both deep analytical work and impactful communication.

To visualize data, we will use Plotly Express. Plotly is a powerful tool for creating data visualizations that cater to both exploration and explanation. Its interactivity and versatility make it ideal for analyzing data dynamically, offering features like hover tooltips, zooming, and filtering to dive deeper into specific details. Plotly’s ease of use and support for advanced techniques, such as animations and subplots, enable users to build compelling, polished visualizations. Analyzing data visualizations created in Plotly allows for a richer understanding of the dataset, as users can interact with the visual representation to focus on areas of interest or investigate anomalies. By combining exploration, explanation, and tools like Plotly, data visualization becomes an indispensable practice for making informed decisions and effectively communicating insights.