Learn about Principal Component Analysis – PCA, this data reduction technique, to identify, quantify & visualise the structure of a set of continuous measurements. PCA provides insightful data visualisation tools. Learn about innovative applications.
Principal component analysis is covered with the help of real case studies.
Course Outline
The most popular version of this course comprises 2 sessions described as follows:
Session 1: The Fundamental Ideas Underlying PCA
- Key Statistical Concepts for PCA
- Traditional Methods for Analysing a Set of Measurements Fundamentals of PCA
- Software Output Through a Case Study – Numerical & Graphical Output
- Variability Explained by Each PC, Correlation & Variance/Covariance Matrix, Loadings & Coordinates of the PCs, Loadings & Coordinates of the PCs
- The Scree Plot Components, Plot of the Loadings, Correlation Circle, Object Map on the New PCs, Biplot
- Specific Analysis Issues
- A Step-by-Step Approach to PCA Case Studies Software Tools for Performing PCA
Session 2: Applications classiques et innovantes
- Classical Applications
- Innovative Application: using PCA before cluster analysis (unsupervised learning), preference mapping, etc.
- Components A Step-by-Step Approach to PCA Case Studies
- Software Tools for Performing PCA Summary
- How to Report PCA Results
Course Duration
The recommended course duration is 2 online sessions.
Target Audience
This course is aimed at engineers, researchers, biologists and depending on the target audience, the course is adapted. On the one hand by using examples specific to the field of application and on the other hand by presenting specific tools or applications, if applicable.
This module introduces concepts in multivariate data analysis methods.
Michel’s class was a wonderful introduction to the power of PCA. I was especially impressed with the newfound ability to organize and present sensory data. Given the large number of measurements that are usually taken with sensory testing, this method provides a simple, visual method that allow us to answer so many of the questions we usually ask about the data.
I enjoyed the PCA class. It was helpful to use some data that we had in house. Michel is very knowledgable in this area. I am anxious to start using it.
This course was excellent value for the money. I took this course and the Intro to Cluster Analysis on consecutive days. Both were well-structured and with plenty of hands-on opportunities; it is suited to both beginners and to those with some experience in the technique. The instructors were familiar with all the software packages used by the students and were able to offer practical advice on getting the desired output. A very practical course; loaded with information I could put to use right away. These two courses were followed by a one-day workshop where students were able to work with their own data. Highly recommended.
This course is very well structured and instructed. I attended both the PCA and cluster analysis session followed by workshop. The instructor (Natalie) is very knowledgeable and very good at explaining difficult statistical problem in a simple way. This course is especially suitable for non-statistician who needs to perform hands on data analysis. This course also exposed students to many different popular statistics packages so you can get a flavor of each of them which helps me a lot in choosing tools in my future research.