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Module 06 of 08  |  Excel & Google Sheets for Data

📈 Charts & Visualization

A chart communicates in seconds what a table of numbers might take minutes to understand. Learn which chart type to use for which situation — and how to build, customize, and clean up charts in Google Sheets.

⏰ ~40 minutes 🎓 Beginner–Intermediate 📋 Requires: Modules 01–05
📌 Before You Start
Choosing the right chart is half the work

The most common visualization mistake is choosing the wrong chart type. A bar chart works great for comparing categories — but terrible for showing trends over time. A pie chart with 12 slices is almost always unreadable. This module walks you through each chart type, when to use it, and when to avoid it. You'll build charts from the Module 05 pivot table data.

💡 The Concept
The Four Most Important Chart Types — When to Use Each

📊 Bar Chart (Column Chart)

Use when: comparing categories

Shows a bar (or column) for each category. Height or length = value. Great for side-by-side comparisons.

Example: Revenue by Region. Revenue by Category. Sales by Salesperson.

📈 Line Chart

Use when: showing trends over time

Connects data points with a line, emphasizing direction and rate of change. Time (dates, months) should always be on the horizontal axis.

Example: Monthly revenue trend. Daily website visits over 6 months.

🟩 Pie Chart

Use when: showing parts of a whole (sparingly!)

Shows percentage composition. Best with 2–5 categories. Over 5 slices becomes hard to read. Humans are poor at comparing angles.

Example: Market share of 3 products. Budget breakdown with 4 categories.

🌟 Scatter Plot

Use when: showing relationship between two variables

Each dot is one data point. X-axis = one variable, Y-axis = another. Shows correlation, clusters, and outliers.

Example: Study hours vs. test scores. Advertising spend vs. sales revenue.
⚠ The Pie Chart Problem: Pie charts are widely overused. Humans are much better at comparing lengths (bar charts) than angles (pie charts). If you have more than 4 categories, use a bar chart instead. If you want to show "this is 80% of the whole", a bar chart with a reference line is often clearer.
🔧 Step-by-Step
Inserting a Chart in Google Sheets

Use the pivot table result from Module 05 (Revenue by Region). The data should look like this:

A — RegionB — Revenue
East3840
North4643
South5245
West3515
  1. Select the data range including headers: A1:B5.
  2. Go to Insert → Chart. A chart is inserted with a Chart editor panel on the right.
  3. Under "Chart type", select Bar chart (or Column chart — same thing, just orientation).
  4. Sheets automatically detects your data — Region on one axis, Revenue on the other.
  5. Click the chart to move or resize it. Drag the corners to resize. Drag the center to move.

Customizing your chart:

  1. Chart title: In the Chart editor, go to the Customize tab → Chart & axis titles → type "Revenue by Region".
  2. Axis titles: Under the same section, add a horizontal axis title ("Region") and a vertical axis title ("Revenue ($)").
  3. Colors: Customize tab → Series → click the color swatch to change bar color. Use your green theme for consistency.
  4. Legend: Customize → Legend → position it or hide it (for a single-series chart, a legend is often unnecessary).
  5. Grid lines and background: Customize → Chart style → toggle background color, border, font.
Excel equivalent: Select data → Insert → Recommended Charts (or choose from the chart gallery). After inserting, use the Chart Design and Format tabs in the ribbon to customize. Right-click any chart element (title, axis, bars) to format it specifically.
🔧 Step-by-Step
Creating a Line Chart for Trends

Use the monthly revenue data from the Module 05 time series pivot (or enter this data manually on a new sheet):

A — MonthB — Revenue
January4305
February3645
March2913
April4775
May2375
June3135
  1. Select A1:B7 and go to Insert → Chart.
  2. Change chart type to Line chart.
  3. Add a title: "Monthly Revenue Trend — 2024".
  4. Add axis labels: horizontal = "Month", vertical = "Revenue ($)".
  5. In Customize → Series, enable "Point size" to make data points visible as dots on the line.
🔧 Step-by-Step
Creating a Scatter Plot

Enter this study data on a new sheet:

A — Study HoursB — Test Score
152
258
364
470
575
682
778
888
991
1095
  1. Select A1:B11 → Insert → Chart → choose Scatter chart.
  2. Add title "Study Hours vs. Test Score".
  3. Add axis labels: horizontal = "Study Hours", vertical = "Test Score (%)".
  4. In Customize → Series, you can add a trendline to show the general direction of the relationship.
💡 Best Practices
Chart Design: Do's and Don'ts

✅ Do

  • Always include a chart title
  • Label your axes clearly
  • Start the Y-axis at zero for bar charts
  • Use consistent colors throughout a report
  • Keep it simple — remove clutter
  • Use color to highlight, not to decorate
  • Choose the right chart type for the data

❌ Don't

  • Use 3D charts (they distort perception)
  • Use pie charts with more than 5 slices
  • Use rainbow colors for no reason
  • Truncate the Y-axis to exaggerate differences
  • Use gridlines so dark they overwhelm the data
  • Skip axis labels and titles
  • Use a chart when a table communicates better
🖐 Your Turn
Exercise: Build Three Charts

Using your Module 05 pivot table data:

  1. Bar chart: Create a bar chart showing Revenue by Region. Title it appropriately. Add axis labels. Change the bar color to green (#43A047).
  2. Line chart: Create a line chart showing Monthly Revenue Trend from the time series pivot (or the sample data above). Add a proper title and both axis labels. Enable data point dots on the line.
  3. Pie chart: Create a pie chart showing Revenue by Category (3 slices: Electronics, Books, Clothing). Notice how easy it is to read with just 3 slices vs. what would happen with 10.

Reflection questions:

Hint: To change bar color in Google Sheets: click the chart, open Chart editor, go to Customize → Series → click the color swatch next to "Color".
🧠 Brain Break
The Most Famous Chart in History

In 1858, Florence Nightingale — known for nursing, but also a pioneer statistician — created a "rose diagram" (now called a polar area chart) to show British Army mortality data. Her visualization proved that most soldiers were dying from preventable infections, not battle wounds. It convinced the government to reform army hospitals. The chart literally saved lives.

This is why visualization matters: the same data that looked like dry numbers in a table became unmistakable in chart form. Good charts change minds and drive decisions.

✅ Wrap Up
Module 06 Key Takeaways

Next: Module 07 — Data Cleaning →