Looking for crea.science HCP targeting solutions website? Follow this link: crea.science
Principal Component Analysis

Principal Component Analysis

Learn about Principal Component Analysis – PCA, this data reduction technique, to identify, quantify & visualise the structure of a set of measurements. PCA provides insightful data visualisation tools. Learn about innovative applications.

  • Key Statistical Concepts for PCA
  • Traditional Methods for Analysing a Set of Measurements Fundamentals of PCA
  • Determining Principal Components (PCs)
  • Software Output Through a Case Study
  • Summary Statistics and Other Table Output
  • Variability Explained by Each PC
  • Correlation & Variance/Covariance Matrix
  • Loadings & Coordinates of the PCsGraphical Output The Scree Plot Components Loadings Correlation Circle Object Map on the New PCs Biplot Specific Analysis Issues Choosing an Adequate Measure of Variability Selecting a Subset of Components A Step-by-Step Approach to PCA Case Studies Software Tools for Performing PCA Summary
This module is intended for all scientific staff who collect large datasets and who wish to graphically summarising them and identify redundancy for the purpose of data reduction.This training course introduces the important ideas in statistics and multivariate data analysis. It assumes that participants have no previous knowledge of statistics or that they have not used it for a long time.
Upon completion of this module, participants will know:
    • The purpose & context of use for PCA
    • The required data layout for running PCA in software packages
    • How to choose the options based on data features
    • How to interpret the software output
    • How to produce informative 2D data maps
    • How to map data, interpret results and draw conclusions

Recommended Duration: 1 day(s)

Course Materials:

TBD

Case Studies:

N/A

This Session Has 4 Reviews

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Comments are closed.