Initial Publication Date: January 17, 2018

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.

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Visualizing Data with MATLAB

Approaches to Getting Students Started

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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.

Example: Using Univariate Statistics to Understand Regional Drainage Patterns by Peter N. Adams (University of Florida). Students use MATLAB to compare two data sets of organic matter content in order to provide quantitative evidence that tests the null hypothesis that sediment samples have the same fluvial source.

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.

Example: Visualizing outcrop patterns from strike/dip data using MATLAB by Charly Bank (University of Toronto). Students explore strike-dip outcrop patterns on a map and in 3D.