# Visualization

Content on this page was originally created for On the Cutting Edge: Teaching with Data, Simulations, and Models and is expanded here. The content is derived from participant presentations, discussions, and breakout groups at the Teaching Computation with MATLAB workshops and benefitted from the editing of Charly Bank, University of Toronto.

Visualizations are used to represent and explain ideas and explore data. Using data visualization is a core skill in the sciences, and there are multiple, important approaches that are commonly used across the STEM fields. Within a particular course or activity the focus may be on the data visualization and interpretation itself, or on learning and practicing the computational skills needed to successfully and appropriately do data visualization. The purpose and design of a course will dictate the balance of coding versus data visualization and when is each emphasized.

Jump down to: Approaches to Getting Students Started | Visualization Resources

## Visualizing Data with MATLAB

MATLAB is a powerful tool for providing students capacity to visualize data from a wide variety of sources and understanding complex systems and for representing complex data. Students can visualize and manipulate data to see how the data behave. Students can also visualize how to model fundamental equations and relationships. In addition to the overarching benefits of MATLAB, MATLAB lends itself to this type of exploration and visualization through:

• flexible and customizable interfaces
• raster and vector data compatability
• map display capabilities

The MATLAB Live Editor provides an interactive and self-contained environment for writing code and creating visualizations. MATLAB Plot Gallery provides examples of many of the ways data can be represented visually in the program.

## Approaches to Getting Students Started

When providing pre-written code to students, they can get started right away with visualizing data. A scaffolded lab can provide students with the very basic skills needed to manipulate and explore the data.

• Students can model responses to different scenarios and parameters; they can compare such models to real data.
• Example: Relaxation Method for a real parallel-plate capacitor by Sean Bartz (Macalester College). Students learn to solve Laplace's equation to determine the capacitance of a parallel-plate capacitor and compare this result to measurements done on a real system.
• MATLAB allows students to quickly produce profiles and scatterplots of data.
• Students can integrate visualization of their own data with simple models, and play with parameters to obtain match. This can be very powerful to understand which parameters may have the biggest effect on the model.
• Example: Curve Fitting Exercise in MATLAB by Wendy Thomas, (University of Washington). Students program using MATLAB to compare the fit of several models to an experimental data set.
• MATLAB GUI and stand-alone runtime approaches can be used for students to work with models without having to know MATLAB. This first step can also get students curious about programming.