Transform raw data into clear, persuasive visual narratives using a structured, hands-on workflow in Microsoft Excel. This one-day course teaches professionals how to eliminate visual clutter and apply cognitive design principles to communicate actionable insights to stakeholders, clients, and broader audiences.
Overview
Syllabus
Module 1: Foundations — Data, Stories, and Audience
- Identify what makes a data story work and distinguish data from information
- Recognize internal and external data sources and understand how data flows across the internet
- Apply audience analysis and learning style awareness to tailor your data story
Module 2: Reading and Perceiving Visualizations
- Interpret a range of chart types including bar, heat map, KPI, stacked, and drilldown visualizations
- Apply visual perception principles — order, hierarchy, clarity, and convention — to evaluate any chart
- Use Gestalt principles, emphasis, and annotation to guide audience attention
Module 3: Building Effective Visualizations
- Select the appropriate visualization type for comparative, time series, correlation, and geographic data
- Use color intentionally and avoid common deceptive chart techniques
- Follow a step-by-step process for building a data story using the analytics value chain
Module 4: Excel for Data Discovery and Analysis
- Perform data discovery and integrity checks to qualify data before analysis
- Use AutoSum, sorting, filtering, and math functions to explore datasets
- Build Pivot Tables and Pivot Charts to summarize and visualize transactional data
Module 5: AI, Data Quality, and Applied Case Studies
- Use AI tools and prompting best practices to confirm and refine a data story
- Apply data quality principles and joining techniques to prepare datasets for analysis
- Complete hands-on case studies covering duplicate analysis, stratification, Benford's Law, sampling, and analysis automation
Taught by
Dan Rodney, Garfield Stinvil, Mourad Kattan, and Christophe Drayton