The Three Datasets You Will Visualize

This course uses three datasets designed to cover the most common business visualization scenarios. Each dataset presents different analytical challenges and visualization opportunities. You will work through all three in both Claude and ChatGPT, building the same visualizations in each tool so you can compare the workflows, outputs, and tradeoffs directly.

1. Dataset 1: Quarterly Revenue Four quarters of revenue data across three products (Widget Pro, DataSync, CloudBase) and three regions (North America, Europe, Asia Pacific). 36 rows with revenue, units sold, and regional breakdown. The analytical questions: Which product is growing fastest? Which region is underperforming? Is revenue growth driven by volume or price? This dataset tests your ability to build comparison charts, trend lines, and composition breakdowns.
2. Dataset 2: Support Tickets Thirty support tickets spanning January through March 2025 with category, priority, resolution time, satisfaction score, assigned agent, and escalation status. The analytical questions: Is there a correlation between priority and resolution time? Which agent handles escalations most effectively? Are technical issues driving dissatisfaction? This dataset tests scatter plots, distributions, and segmented analysis.
3. Dataset 3: Marketing Funnel Three months of marketing data across five channels (LinkedIn Ads, Google Search, Email, Content/SEO, Webinars) with full-funnel metrics from impressions through closed-won deals plus spend data. The analytical questions: Which channel has the best conversion rate? Where does each funnel leak most? What is the cost per acquisition by channel? This dataset tests funnel charts, efficiency metrics, and multi-variable comparison.

Copy each dataset below into a file on your machine. Save them as CSV files — quarterly-revenue.csv, support-tickets.csv, and marketing-funnel.csv. You will paste these into both Claude and ChatGPT throughout the course.

Quarter,Product,Revenue,Units Sold,Region
Q1 2025,Widget Pro,142000,1847,North America
Q1 2025,Widget Pro,89000,1156,Europe
Q1 2025,Widget Pro,63000,819,Asia Pacific
Q1 2025,DataSync,218000,436,North America
Q1 2025,DataSync,167000,334,Europe
Q1 2025,DataSync,94000,188,Asia Pacific
Q1 2025,CloudBase,312000,260,North America
Q1 2025,CloudBase,198000,165,Europe
Q1 2025,CloudBase,87000,73,Asia Pacific
Q2 2025,Widget Pro,158000,2053,North America
Q2 2025,Widget Pro,102000,1326,Europe
Q2 2025,Widget Pro,71000,923,Asia Pacific
Q2 2025,DataSync,234000,468,North America
Q2 2025,DataSync,189000,378,Europe
Q2 2025,DataSync,112000,224,Asia Pacific
Q2 2025,CloudBase,347000,289,North America
Q2 2025,CloudBase,221000,184,Europe
Q2 2025,CloudBase,103000,86,Asia Pacific
Q3 2025,Widget Pro,131000,1703,North America
Q3 2025,Widget Pro,94000,1222,Europe
Q3 2025,Widget Pro,82000,1066,Asia Pacific
Q3 2025,DataSync,256000,512,North America
Q3 2025,DataSync,201000,402,Europe
Q3 2025,DataSync,143000,286,Asia Pacific
Q3 2025,CloudBase,389000,324,North America
Q3 2025,CloudBase,267000,223,Europe
Q3 2025,CloudBase,134000,112,Asia Pacific
Q4 2025,Widget Pro,174000,2262,North America
Q4 2025,Widget Pro,118000,1534,Europe
Q4 2025,Widget Pro,97000,1261,Asia Pacific
Q4 2025,DataSync,278000,556,North America
Q4 2025,DataSync,223000,446,Europe
Q4 2025,DataSync,168000,336,Asia Pacific
Q4 2025,CloudBase,423000,353,North America
Q4 2025,CloudBase,312000,260,Europe
Q4 2025,CloudBase,167000,139,Asia Pacific

Date,Category,Priority,Resolution Hours,Satisfaction,Agent,Escalated
2025-01-03,Billing,High,2.4,4.2,Sarah,No
2025-01-05,Technical,Critical,1.1,3.8,Marcus,Yes
2025-01-07,Onboarding,Medium,4.7,4.6,Sarah,No
2025-01-09,Technical,High,3.2,3.5,Priya,Yes
2025-01-12,Billing,Low,6.8,4.8,Marcus,No
2025-01-14,Feature Request,Medium,8.3,4.1,Sarah,No
2025-01-16,Technical,Critical,0.8,3.2,Priya,Yes
2025-01-19,Onboarding,Low,5.1,4.9,Marcus,No
2025-01-22,Billing,Medium,3.9,4.4,Sarah,No
2025-01-24,Technical,High,2.7,3.6,Priya,Yes
2025-01-27,Feature Request,Low,12.4,4.3,Marcus,No
2025-01-30,Technical,Critical,1.3,3.1,Sarah,Yes
2025-02-02,Onboarding,Medium,4.2,4.7,Priya,No
2025-02-05,Billing,High,2.1,4.5,Marcus,No
2025-02-08,Technical,High,2.9,3.4,Sarah,Yes
2025-02-11,Feature Request,Medium,9.7,4.0,Priya,No
2025-02-14,Technical,Critical,0.9,3.3,Marcus,Yes
2025-02-17,Onboarding,Low,5.6,4.8,Sarah,No
2025-02-20,Billing,Medium,3.4,4.6,Priya,No
2025-02-23,Technical,High,2.3,3.7,Marcus,Yes
2025-02-26,Technical,Medium,4.1,4.2,Sarah,No
2025-03-01,Billing,Low,7.2,4.9,Priya,No
2025-03-04,Technical,Critical,1.0,3.0,Marcus,Yes
2025-03-07,Onboarding,High,3.3,4.4,Sarah,No
2025-03-10,Feature Request,Medium,10.2,4.1,Priya,No
2025-03-13,Technical,High,2.6,3.5,Marcus,Yes
2025-03-16,Billing,Medium,3.7,4.5,Sarah,No
2025-03-19,Technical,Critical,1.2,3.2,Priya,Yes
2025-03-22,Onboarding,Low,5.9,4.7,Marcus,No
2025-03-25,Technical,High,2.8,3.6,Sarah,Yes

Month,Channel,Impressions,Clicks,Leads,MQLs,SQLs,Opportunities,Closed Won,Spend
Jan 2025,LinkedIn Ads,284000,8520,426,128,51,19,7,12400
Jan 2025,Google Search,192000,11520,461,184,74,31,12,18600
Jan 2025,Email Campaigns,45000,6750,338,169,68,27,11,2800
Jan 2025,Content/SEO,167000,5010,251,100,40,16,6,4200
Jan 2025,Webinars,8200,3280,656,262,105,42,14,6800
Feb 2025,LinkedIn Ads,312000,9360,468,140,56,21,8,13200
Feb 2025,Google Search,208000,12480,499,200,80,33,13,19400
Feb 2025,Email Campaigns,48000,7200,360,180,72,29,12,3100
Feb 2025,Content/SEO,189000,5670,284,113,45,18,7,4200
Feb 2025,Webinars,9100,3640,728,291,116,46,16,7400
Mar 2025,LinkedIn Ads,298000,8940,447,134,54,20,7,12800
Mar 2025,Google Search,221000,13260,530,212,85,35,14,20100
Mar 2025,Email Campaigns,51000,7650,383,191,77,31,13,3400
Mar 2025,Content/SEO,203000,6090,305,122,49,19,8,4200
Mar 2025,Webinars,10400,4160,832,333,133,53,18,8200