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LLEF10174U Exploratory Data Analysis
Full Degree Master
Continuing and further
Study Board of Food, Human Nutrition and
Department of Food Science
Åsmund Rinnan (3-69697a486e77776c36737d366c73)
Saved on the
MSc Programme in Food Science and Technology
MSc Programme in Agriculture
MSc Programme in BIology- Biotechnology
In industry and research huge amounts of
physical, chemical, sensory and other quality measurements are
produced on all sorts of materials, processes and products.
Exploratory data analysis / chemometrics offers a tool for
extracting the optimal information from these data sets through the
use of modern software and computer technology.
The course will give a step-by-step theoretical introduction to
exploratory data analysis / chemometrics supported by practical
examples from food science, agro technology, medicine,
pharmaceutical science etc.
Methods for exploratory analysis (Principal Component Analysis),
multivariate calibration (Partial Least Squares) and basic data
preprocessing are considered. Understanding and interpretation of
the computed models is central. As is methods for outlier detection
and model validation. Computer exercises and the project will be
performed applying user-friendly software. A thorough introduction
to the software will be given.
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 student 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
Apply theory on real life data analytical cases
Apply commercial software for data analysis
Report in writing a full data analysis of a given problem including
all aspects presented under Knowledge.
Interpret multivariate models (both exploratory and regression)
Discuss and respond to univariate versus multivariate data
analytical methodology in problem solving in society
Notes, papers and other course material.
Teaching and learning methods
Lectures, guest lectures, cases, workshops and
computer exercises will introduce the chemometric theory and the
practical aspects of multivariate data analysis. In the project
real data analytical problems are solved from a methodological
perspective and the results are reported in written form. The
project will mainly be based on data sets from the Spectroscopic
and Chemometrics group, Quality & Technology, Department of