Exploratory Data Analysis / Chemometrics

In industry and research, huge amounts of physical, chemical, sensory and other quality measurements are produced on all sorts of materials, processes and products. This course offers a tool for extracting the optimal information from these data sets through the use of modern software and computer technology.

The course introduces basic chemometric methods (PCA and PLS) and their use on different kinds of multivariate data of relevance for research and development. Furthermore, the exploratory element in research and development is illustrated.

After completing the course the participant should be able to:

  • Describe chemometric methods for multivariate data analysis (exploration and regression)
  • Describe techniques for data pre-preprocessing
  • Describe techniques for outlier detection
  • Describe method validation principles
  • Describe methods for variable selection
  • Understand the basics of the algorithms behind the PCA and PLS
  • Understand the math of data pre-processing