I analyzed a dataset of beer styles to explore the relationship between alcohol content (ABV) and bitterness (IBU), performing data preparation, exploration, and analysis in Excel. After cleaning and organizing the dataset, I used pivot tables and summary statistics to examine variations across beer styles and identify key trends in strength and bitterness.

I then developed visualizations in Excel, including bar charts, histograms, and a scatter plot, to highlight differences in ABV and IBU distributions and uncover patterns across categories. The analysis revealed a moderate positive relationship between alcohol content and bitterness, as well as clear clustering of most beers within mid-range values, with outliers representing specialty styles.

My analysis revealed:

• Most beer styles fall within moderate ranges of 4–7% ABV and 20–70 IBU, indicating a strong market preference for balanced, approachable beers.
• A positive relationship exists between alcohol content and bitterness, with stronger beers generally exhibiting higher IBU levels.
• High-ABV and high-IBU outliers represent specialty or niche styles, such as Imperial Stouts and Barleywines.

Project Workflow:

• Cleaned and prepared the dataset in Excel, removing inconsistencies and organizing beer styles into structured categories
• Calculated summary statistics to understand average ABV and IBU across beer types
• Used pivot tables to analyze variations in strength and bitterness by beer style
• Built bar charts to compare average ABV and IBU across categories
• Created histograms to examine the distribution of ABV and IBU values
• Developed a scatter plot to visualize the relationship between alcohol content and bitterness
• Interpreted results to identify patterns, trends, and outliers