Storytelling in Data [GK840037]

OVERVIEW

Storytelling in Data will explore the concept of Data Visualization, understanding what it is, the various types of charts, and how to choose the right visualization for the data at hand. Learners will also be introduced to Generative AI Tools for data visualization, which are essential in the modern data-driven world.

This comprehensive course aims to equip learners with a solid foundation in data storytelling and visualization and in creating a compelling data story, along with how to anticipate potential barriers.

OBJECTIVES

• Learn chart types and when to use them.
• Evaluate generative AI tools for data visualization.
• Use visual design elements to create effective data visualizations.
• Encode data with color meaningfully and follow accessibility guidelines.
• Evaluate data quality through data collection, cleaning, and compiling stages.
• Avoid misleading data visualizations.
• Balance making an argument with representing data accurately.
• Tailor data visualizations to specific audiences.
• Address barriers that audiences face in understanding or taking action.
• Ethically communicate data through data storytelling.
  

AUDIENCE

Data analysts, data visualization practitioners, and technical AI users who want to create more compelling arguments with their data, and learn how to combine data, text, and visualizations to craft a data story.

CONTENT

1- Introduction to Data Storytelling 

• What is Data Visualization?
• Types of Charts and Choosing the Right Visualization
• Generative AI Tools for Data Visualization

2- Visual Design Theory 

• Elements of Visual Design
• Limitations of Generative AI in Design
• Grouping and Highlighting Data to Build and Argument

3- Analyzing Misleading Visualizations 

• Data Quality and Collection
• Accuracy in Scale and Context
• Reporting and Misrepresentation of Statistics

4- Data Storytelling 

• Introduction to Data Storytelling
• Key Elements of a Compelling Data Story
• Anticipating Audience Barriers

5- Data for Your Audience 

• Tailoring Insights to the Audience
• Structure of a Compelling Data Story
• Ethics and Truth in Data Storytelling
• Reducing Friction for the Audience.