Stop hiding workforce patterns in charts that mislead—learn to visualize turnover data that stakeholders can actually read, and use AI to articulate your findings clearly. In this hands-on course, you'll discover why chart type selection is not cosmetic, mastering the principle that a 3% difference visible in a bar chart can vanish in a pie chart. You'll learn to build clustered bar charts in Excel comparing quarterly turnover by gender, format them with clear labels and legends, and use ChatGPT or Claude to articulate the patterns in stakeholder-friendly language. Most importantly, you'll develop the ability to critically evaluate AI-generated descriptions and defend your visualization choices, explaining why clustered bars communicate comparison data more effectively than pie charts. Through realistic role plays and coach dialogues, you'll practice advising colleagues on chart selection, prompting AI for pattern articulation, and completing full visualization workflows. Designed for HR generalists, HR analysts, and people managers who present workforce metrics. Basic familiarity with Excel is helpful; no data visualization background is required.

Chart HR Trends

Chart HR Trends
This course is part of People Data Foundations: Metrics & Visualization Specialization


Instructors: Coursera Support
Access provided by Willis Towers Watson
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Explain why chart type selection matters for interpreting workforce turnover trends clearly and accurately.
Build a clustered bar chart in Excel comparing quarterly turnover by gender.
Use AI to articulate patterns and explain why clustered bars communicate comparison data better than pie charts, reducing HR metrics misreading.
Details to know

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Assessments
2 assignments
Taught in English
Recently updated!
April 2026
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This course is part of the People Data Foundations: Metrics & Visualization Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
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