Capstone: Build a Complete Dashboard

Everything Comes Together

🔄 Quick Recall: Over seven lessons, you’ve built a complete spreadsheet toolkit. From formulas (Lesson 2) to data organization (Lesson 3), from lookups (Lesson 4) to pivot tables (Lesson 5), from visualization (Lesson 6) to automation (Lesson 7). Now you combine everything into one capstone project.

The Capstone Project

Your assignment: Build a complete, automated dashboard from raw data. Use every major technique from this course.

Choose one of these scenarios (or use your own data):

• Sales dashboard: Track revenue, orders, and performance by product, region, and time period
• Personal finance dashboard: Monitor income, expenses, savings rate, and budget vs. actual by category
• Project tracking dashboard: Show task completion, timeline status, and resource allocation
• Marketing dashboard: Display campaign performance, conversion rates, and ROI by channel

Step 1: Structure Your Data (10 minutes)

Apply Lesson 3 principles to prepare your data:

I'm building a [type] dashboard.
My raw data has these columns:
[list your columns]

Help me:
1. Verify the data is in flat table format
2. Identify any cleaning needed (duplicates, spaces,
   inconsistent values)
3. Suggest data validation rules for each column
4. Recommend any helper columns to add
5. Create the data as a formal table

✅ Quick Check: Before building formulas, can you recall the data structure rules from Lesson 3? (One row per record, one column per attribute, no blank rows, no merged cells, one header row.)

Step 2: Build Formulas and Lookups (10 minutes)

Add calculated columns using techniques from Lessons 2 and 4:

My data table has: [list columns]

I need these calculated fields:
1. [Describe calculation 1]
2. [Describe calculation 2]
3. [Describe calculation 3]

Write the formulas using:
- IF for categorization
- SUMIFS for conditional totals
- INDEX-MATCH for cross-referencing
- Named ranges where appropriate

Step 3: Create Pivot Tables (10 minutes)

Build 2-3 pivot tables for different views of your data (Lesson 5):

• Pivot 1: Summary by primary category (e.g., sales by region)
• Pivot 2: Trend over time (e.g., monthly totals)
• Pivot 3: Detailed breakdown (e.g., product by region by month)

Step 4: Design the Dashboard (15 minutes)

Assemble the dashboard sheet using Lesson 6 principles:

I've built pivot tables showing:
- [Pivot 1 summary]
- [Pivot 2 summary]
- [Pivot 3 summary]

Design my dashboard layout:
1. TOP ROW: Which key metrics as big numbers?
2. MAIN AREA: Which 2-3 charts (specify types)?
3. SUPPORTING: Which tables or detail views?
4. What slicers/filters for interactivity?
5. Color scheme and formatting guidelines?

Step 5: Add Automation (10 minutes)

Apply Lesson 7 techniques to make the dashboard self-updating:

• Protect formula cells from accidental editing
• Add data validation to the input sheet
• Create a clear “paste data here” zone
• Add instructions for updating with new data
• Test by adding new rows and verifying everything updates

The Quality Checklist

Before considering your dashboard complete:

ACCURACY
[ ] Spot-check: Dashboard totals match raw data totals
[ ] Formulas return correct values with test data
[ ] No #REF!, #N/A, or #VALUE! errors visible

FUNCTIONALITY
[ ] Charts update when pivot tables change
[ ] Slicers/filters work correctly
[ ] New data rows are included automatically
[ ] Template works when data is cleared and re-entered

DESIGN
[ ] Chart titles state insights, not just labels
[ ] No 3D charts or excessive decoration
[ ] Consistent color scheme across all charts
[ ] Key metrics visible at the top

PROTECTION
[ ] Formula cells are protected
[ ] Input cells are clearly marked
[ ] Instructions included for other users
[ ] Version number noted

Course Summary

Here’s every technique from this course organized as a permanent reference:

Key Takeaways

• The complete dashboard pipeline flows from data cleaning through formulas, pivot tables, visualization, and automation
• Always verify accuracy before sharing: spot-check totals against source data
• Maintainability matters: named ranges, documentation, and separated data/presentation zones keep dashboards working over time
• AI accelerates every step but you must verify the outputs

Congratulations

You’ve gone from “I know the basics” to building professional, automated dashboards that would impress any team. The spreadsheet skills you’ve learned aren’t just about Excel or Google Sheets—they’re about thinking systematically about data, structure, and communication.

Every time you open a spreadsheet from now on, you’ll see opportunities: a SUMIF where someone is manually adding, a pivot table where someone is creating formulas row by row, a dashboard where someone is rebuilding a report from scratch every week.

You have the tools to save hours, eliminate errors, and communicate data clearly. AI amplifies these skills by handling the formula syntax, suggesting chart types, and debugging errors. But the thinking—what question to ask, how to structure the analysis, what insight to highlight—that’s yours.

Go build something useful.