Exploratory Data Analysis and Visualization

After cleaning and preparing the dataset, the next step is to explore the data and understand patterns, trends, and relationships within sales. Exploratory Data Analysis, often called EDA, helps convert raw numbers into meaningful insights using summaries and visualizations.

In this project, EDA focuses on understanding how revenue changes over time, which products contribute the most to sales, how revenue is distributed across quarters, and which customers generate the highest revenue. These insights are easier to understand when represented visually.

To study revenue trends over time, sales data is grouped by month and year and visualized using a line chart. Line charts are useful because they clearly show how values increase or decrease over a continuous period. This helps identify seasonal trends and recurring patterns in monthly sales.

Bar charts are used to compare revenue across categories such as product lines and customers. Bar charts make it easy to see which categories perform better by comparing the height of bars. This is especially helpful when identifying top-performing products or high-value customers.

Comparative bar charts are used to analyze quarterly revenue across different years. By placing multiple bars side by side, this type of chart allows easy comparison between quarters and highlights differences in performance over time.

Through these visualizations, the sales data becomes more intuitive and easier to interpret. EDA prepares the foundation for drawing conclusions and business insights, which will be covered in the next module.