Data becomes more useful when it is presented as a story people can quickly understand and act on. In this hands-on course, you will learn how to turn raw data into clear, compelling visual narratives in Microsoft Excel. You will follow a practical storytelling workflow that covers audience analysis, chart selection, clutter reduction, visual emphasis, and the delivery of insights that support better decision-making.
Built on cognitive science and design thinking principles, this course goes beyond the mechanics of creating charts to show why certain visual choices are more effective than others. Through real-world examples and applied exercises, you will strengthen your ability to communicate complex information with clarity, credibility, and impact across presentations, reports, and other professional settings.
What You Will Learn
- Apply a structured framework to define context, audience, and narrative goals before creating any visualization
- Select the most effective chart type based on data relationships and intended message
- Recognize and eliminate visual clutter that obscures your data's meaning
- Apply preattentive attributes and Gestalt principles to direct audience attention
- Think like a designer to create accessible, aesthetic, and intuitive visualizations
- Structure a data-driven narrative using proven storytelling techniques
- Avoid common visualization mistakes and ethical pitfalls
- Confidently present data stories that inform, persuade, and inspire action
Course Outline
Module 1: Foundations — Data, Stories, and Audience
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Identify what makes a data story work and distinguish data from information
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Recognize internal and external data sources and understand how data flows across the internet
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Apply audience analysis and learning style awareness to tailor your data story
Module 2: Reading and Perceiving Visualizations
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Interpret a range of chart types including bar, heat map, KPI, stacked, and drilldown visualizations
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Apply visual perception principles — order, hierarchy, clarity, and convention — to evaluate any chart
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Use Gestalt principles, emphasis, and annotation to guide audience attention
Module 3: Building Effective Visualizations
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Select the appropriate visualization type for comparative, time series, correlation, and geographic data
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Use color intentionally and avoid common deceptive chart techniques
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Follow a step-by-step process for building a data story using the analytics value chain
Module 4: Excel for Data Discovery and Analysis
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Perform data discovery and integrity checks to qualify data before analysis
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Use AutoSum, sorting, filtering, and math functions to explore datasets
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Build Pivot Tables and Pivot Charts to summarize and visualize transactional data
Module 5: AI, Data Quality, and Applied Case Studies
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Use AI tools and prompting best practices to confirm and refine a data story
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Apply data quality principles and joining techniques to prepare datasets for analysis
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Complete hands-on case studies covering duplicate analysis, stratification, Benford's Law, sampling, and analysis automation
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
