Data Visualization and Storytelling: Fundamentals of Communicating with Data

Communication is the final stretch of a data analysis journey. Analysis, discovery, insight, forecasting, prediction … none of these have real impact until the results are communicated and understood. Data visualization is an important part of analytics and the foundation of communicating with data.

Effectiveness and value of analytics and data science depend on communication capabilities of three kinds – abilities to create, read, and explain data visualizations.

Creating and reading visualizations are language skills for working with the language of images. With word-based languages we learn the basic literacy skills of reading and writing early in life. We understand nouns, verbs, adjectives, grammar and syntax through experiences and lessons. With image-based languages we must learn similar concepts of points, lines, shapes, axes, scales, coordinates, visual cues, etc. These are fundamental visualization literacy skills.

Explaining data visualizations draws on the art of storytelling, and data storytellers have a central role in communicating with data. Data storytelling adds narrative to interpret and explain data visualizations. At minimum, narrative enriches visualizations, describes conclusions, increases understanding, and avoids misunderstanding and miscommunication. At its best, storytelling goes beyond explanation to engage, energize, excite, inspire, and motivate people.

You Will Learn:

• Ten key concepts of data visualization
• “Quick read” and “critical read” techniques for reading data visualizations
• How to see trends, patterns, ambiguity, distortion, and bias in data visualizations
• Data visualization techniques for data analysis
• Data visualization techniques for business intelligence
• Data visualization techniques for data exploration
• How to interpret data visualizations and find stories in the data
• How to compose captivating and compelling data stories