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