Data Visualization Principles for Scientists

Course Description

Participants will earn the basic principles of scientific data visualization. Topics will include the appropriate choice of colour and how to effectively choose and design plots. The course will include many data examples, good and bad, and will include an opportunity to redesign a visual based on the principles described in the course.

Course Outline

We begin the course with a motivation of why data visualization matters. I will show good, bad and ugly visualizations and we will dissect these to understand what makes a good data visualization and how some plots deceive - sometimes intentionally! Next, we will take a fast historical tour taking in pivotal plots from, among others, Florence Nightingale, William Playfair, John W. Tukey and Edward Tufte.

Next, we will learn about human vision and how we can exploit our biology to efficiently process charts. We will have a deep dive into human colour perception to discuss issues around colour vision deficiencies and how we can reach a wider audience by a careful choice of colours. We continue on the theme of human visual perception by reviewing pre-attentive attributes and the Gesthalt principles and how these can be employed in our charts. We will pay particular attention to line charts, pie chart and why 3D effects should only be used with caution.

Our last taught session will review typefaces (fonts), tables and file formats before we move to a practical session where participants will be asked to choose a plot, identify any shortcomings and then redesign it based on the principles we have learnt during the day.

Course topics include:

  1. Why data visualization matters
  2. Short History
  3. Human Vision
  4. Colour Palettes
  5. Preattentive attributes & Gesthalt principles
  6. Line charts, Pie & Donut Charts, 3D effects
  7. Typefaces
  8. Tables & Layouts
  9. File formats (JPEG, PNG, SVG etc)
  10. Visualization makeover

Participants’ Profile

This course is for anyone who presents or interprets scientific data. Participants will learn how to create efficient data visualizations by choosing appropriate chart types and applying good design principles.

Prerequisites

Participants should be reasonably confident with a data visualization tool e.g. Excel, Matlab, python etc. as we will be performing a data visualization makeover in our last session of the day.

About the Instructor

Steve Horne is a research physicist who has worked in the seismic and defence industries for over 30 years in small start-up companies, major integrated companies, and service providers in locations around the world. His fascination with scientific visualization began in 2004 whilst acting as an associate editor for Geophysical Prospecting. Over the course of the next 20 years Steve continued to investigate and collect best practices in scientific visualization. Steve would like to share what he has learnt to improve the quality of visual communication for the benefit of the scientific community.