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 or Solo.

Prerequisites

Chemometrics I--PCA and Chemometrics II--Regression and PLS or equivalent experience.

Course Outline

1. Overview and Motivation
   1.1 Data Structures and Features (continuous vs. discrete... do you have classes and labels etc)
   1.2 Exploratory vs. Explanatory (what's your goal with any particular plot)
   1.3 Practical Considerations (importing data, size, techniques used to model, what's important to see)
   1.4 Basic Plotting Elements (what are the basic elements of an axes)
   1.5 Matlab and PLS_Toolbox Plotting Organization (dataset objects, axes, figures, etc)
   1.6 Chemometric Data Discussion (raw data vs a model vs meta data)
2. Statistical Graphics and Exploratory Analysis
   2.1 Scatter Plots (scores plot)
   2.2 Line Plots (spectra)
   2.3 Bar Charts and Histograms (loadings)
   2.4 Correlation Plots (corrmap)
   2.5 Box Plots (and other basic stats such as QQ plots)
3. Advanced Plotting
   3.1 Adding Text (label and class info)
   3.2 Adding Dimension (colorby and class plotting)
   3.3 Z-data (3D)
   3.4 Fitting Lines (basic overlays)
   3.5 Images and Image Data
   3.6 Reverse Engineering (how to find info on what plotgui does)
4. High Quality Graphics
   4.1 Spawning Static Plots (plotgui)
   4.2 Creating Publication Quality Graphics
   4.3 Exporting (to image)
   4.4 Motion and Video (layering etc.)
5. Conclusions
   5.1 Resources for Further Study
   5.2 Future Considerations