Data Visualization

Course Description

This course gives an overview of common data visualization techniques and considerations. Attention will given to exploratory visualization particular to chemometrics. We’ll explore the elements of different visual representations of data, the tools used to create plots, and ideas on how to enhance your visualizations. Along the way we’ll see how Matlab and Eigenvector plotting tools work and how to use them. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox and MIA_Toolbox, or Solo+MIA.

Prerequisites

Chemometrics I — PCA, and Chemometrics II – Regression and PLS or equivalent experience.

Course Outline

  1. Overview and Motivation
    1. Data Structures and Features
    2. Exploratory vs. Explanatory
    3. Practical Considerations
    4. Basic Plotting Elements
    5. Matlab and PLS_Toolbox Plotting Organization
  2. Plot Types
    1. Popular types
    2. Images
  3. Custom Plotting
    1. Defaults (Matlab, Plotgui)
    2. Custom Class Symbols
    3. Reverse Engineering
    4. Fonts
    5. Static Plots
    6. Movies
  4. Exporting
    1. SVG vs Bitmap
    2. Publications
  5. Higher Order Data
    1. Colorby
    2. Layering
    3. Tiled plots
    4. Condensing Data with Factor Based Models
    5. Slicing and Motion
  6. Conclusions
    1. Resources
    2. Future Considerations