Process monitoring, quality assurance and market research routinely collect large amounts of data, the information content of which is often only partly evaluated, as the important information is often hidden in the combinations of data and the traditional statistical analysis methods quickly reach their limits , Multivariate methods such as the PCA (Principal Component Analysis) or the PLS (Projection to Latent Structures) can reduce the information hidden in the data to a few dimensions, thus enabling easy visual presentation and interpretation of the data.
Participants in our courses will learn to quickly and reliably interpret large datasets with advanced multivariate data analysis. In addition to theory, the courses include numerous demonstrations of examples and independent editing of real-life data in order to learn how the multivariate methods are applicable in practice. The exercises can also be performed on separate datasets with the help of the instructor. Our goal is to be able to understand the most important methods of multivariate data analysis after completing the course and to apply them successfully in your own work.
Who should attend?
You have data - you need information
You work in Research & Development with product development, process optimization, quality control and monitoring or consumer research.
You work with spectroscopic methods (IR, NIR, Raman, UV / VIS, NMR), spectrometric (MS) or chromatographic data (GC, LC, HPLC).
You want to evaluate existing data from production, research, sensor technology, or quality control.
To attend the course no prior knowledge is required.