Principles of Data Visualization

This seminar explores the fundamental principles behind creating clear, impactful, and honest data visualizations. Effective graphics are essential for making research credible and memorable, but they can also unintentionally confuse or mislead. This course focuses on the "why" of visualization - the core concepts of design and perception - rather than the "how" of coding. The goal is to equip you with the critical skills to design and interpret charts that communicate your research findings with clarity and integrity.

Please note that this is not a programming course, but a seminar on principles of visualizations.

Target Audience

Researchers at all career stages who want to improve their ability to design, interpret, and communicate with data visualizations, regardless of the software they use.

Topics

The seminar covers the essential elements of effective visual communication:

• The Power and Pitfalls of Visualization: What makes graphics effective, and how to spot misleading charts.
• Core Principles of Visual Design: Using elements like color, shape, and layout to guide interpretation.
• Telling a Story with Data: How to select the right chart type to communicate a clear and compelling message.
• A Framework for Better Graphics: Deconstructing real-world examples to build a practical checklist for improving your own work.

If you would like to learn how to code more advanced graphics in R, please check out our course Graphics with R. If you would like to learn which plots to use for what kind of data, we recommend our course Introduction to Statistics or our advanced statistics courses.

Methods

The course is structured as a highly interactive seminar. It combines presentations on core concepts with facilitated discussions, group exercises, and the critical analysis of real-world visualizations. Active participation is essential, and you will be encouraged to share and discuss examples.

Learning Goals

At the end of this course, you will be able to:

1. Articulate the key principles that underpin effective data visualization.
2. Critically evaluate scientific graphics for clarity, accuracy, and impact.
3. Identify common design flaws that can mislead or confuse an audience.
4. Choose an appropriate visualization type to answer a specific research question.
5. Develop a systematic approach for designing clear and compelling figures for publication.

Prerequisites

None. This is a non-technical seminar and requires no prior programming experience.

Format

• Duration: 3.5 hours
• Language: English
• This course will be offered either on campus (in person), or online.
• For online courses we use the software Zoom.

Dates and Application

• Workshops provided for Helmholtz Munich:
  • You can check the current dates and whether the courses are already fully booked here*.
  • Please read the corresponding FAQ* before applying via the forms of CaMS*.

 * Links marked with * are only available for Helmholtz Munich staff.