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Data Visualization 101 modules

Introduction

This repository contains materials that can be used in various combinations to teach introductory data visualization topics and skills. Please feel free to use these modules, repurpose and remix this content, add suggestions to this project, etc.!

Each of the Data Visualization 101 modules has been recorded and shared in a YouTube playlist.

This project was developed by the Visualizing the Future team and was made possible in part by the Institute of Museum and Library Services, RE-73-18-0059-18.

Contents

How to teach this course

We provide two starting points for teaching Data Visualization 101 modules:

1. A single, self-contained instructor slideshow

This slideshow includes everything you’ll need to lead one or more of the modules. In addition to learner-facing slides, there are also hidden instructor slides, speaker notes, etc.

2. Outlines of complementary activities

We’ve compiled a series of possible activities that would be a good complement to the various modules included in Data Visualization 101. We suggested including at least one hands-on activity within each hour of teaching, and the included activities can often be modified to increase or reduce the amount of time they need.

Proposed timeline

We suggest the following sequences of the course modules for different workshop formats.

1. Recommended 1-hour sequence

  • What is data visualization?
  • Key parts of a data visualization
  • Types of data visualizations and examples
  • Approaching visualizations with a critical eye
  • Activities (max. 15 minutes)

2. Recommended 2-hour sequence

  • What is data visualization?
  • Key parts of a data visualization
  • Types of data visualizations and examples
  • Approaching visualizations with a critical eye
  • Preparing and understanding data
  • How are data visualizations made?
  • Activities (max. 45 minutes)

3. Recommended full sequence (1-2 days)

  • What is data visualization?
  • Visual principles/properties
  • Key parts of a data visualization
  • Types of data visualizations and examples
  • Approaching visualizations with a critical eye
  • Preparing and understanding data
  • How are data visualizations made?
  • Ethics in data visualization
  • Designing for accessibility
  • Designing for reproducibility
  • Activities

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