Data Visualization

In Telling Stories with Data, participants learn how to recognize poorly designed visuals, use visuals to communicate data effectively, leverage white space, color, and preattentive attributes to craft actionable visuals, list the 12 different types of visuals, use the 5-why technique to recognize potential obstacles and identify root causes, distinguish between explanatory and exploratory graphs and charts, build presentation stories and narratives, and summarize the story through talking points.


Introduction to the Data Analytics Pipeline

  • Overview and Learning Objectives

Introduction and Overview

  • Common Issues with Charts and Graphs
  • Common Challenges in Visual Communication

Essential Concepts

  • Presentations Need to Tell a Story
  • Explanatory vs. Exploratory Analysis
  • Types of Presentations
  • The Ten Terrible Turn-offs
  • Audience Analysis
  • Storyboarding & Planning a Presentation
    • Collecting of Key Data Points and Outcomes
    • Use of Sticky Notes and Index Cards
    • Creating Concept Maps

Choosing Effective Visuals

  • Common Visual Types
  • Choosing Appropriate Chart Types
  • Avoiding Clutter
  • Exploiting White Space
  • Preattentive Attributes and Color

Creating Presentation Stories

  • How to Build a Story
  • Developing Talking Points
    • Using the "5-Why" Technique
    • Structuring Talking Points
    • Crafting Convincing Narratives
  • Getting Buy-in

Data Visualization Summary

Practical Application and Discussions

Through a variety of exercises and discussions, participants learn best practices and how to overcome challenges faced when presenting, communicating, and analyzing data.

Participant Presentations — Pulling It All Together

Participants present their "data stories" and receive valuable feedback from the facilitator and peers for how to improve their "data story" presentations.

The Data Analytics Pipeline

Collection > Shaping > Storage > Retrieval > Visualization > Analysis > Communication