For decades, multivariate data analysis (MVA) has been used by data analysts, spectroscopists, analytical chemists, process engineers and sensory scientists to find important relationships in data with multiple variables. Modelling these relationships has proven useful to solve real-world problems in fields ranging from petrochemicals and pharma to agriculture and food industry.
This course builds on concepts introduced in the Camo course Multivariate Data Analysis – Level 1. It is thus targeted to those practitioners who have a fundamental understanding of principal component analysis (PCA) and partial least squares (PLS) regression and are interested in enhancing their knowledge and skills on additional multivariate methods and practical aspects of applying multivariate models.
All course material will be supplemented with hands on practical exercises that highlight the use of the methods and tools discussed in a practical manner.
- Interpreting latent variable models
- Improving your calibration model
- Multivariate prediction
- Multivariate classification
- Big or multiple data sets
- MVA in the age of AI
Good understanding of MVA methods in line with the Camo Multivariate Data Analysis – Level 1 course.
To be determined.
Contact our training coordinator Leslie Euceda at email@example.com phone: +47 22 39 63 00.
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