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Multivariate Data Analysis - 2.5 ECTS - 2024
Provider: Faculty of Science

Activity no.: 5336-24-04-31 
Enrollment deadline: 22/11/2024
PlaceDepartment of Food Science
Date and time02.12.2024, at: 09:00 - 06.12.2024, at: 16:00
Regular seats45
ECTS credits2.50
Contact personHenriette Hansen    E-mail address: henha@food.ku.dk
Enrolment Handling/Course OrganiserÅsmund Rinnan    E-mail address: aar@food.ku.dk
Written languageEnglish
Teaching languageEnglish
Exam requirementsA short report of three pages with data analysis on own/ provided data to be handed in and approved. This report may be written in groups of two.
Exam formCourse Participation; Written Assignment
Course workload
Course workload categoryHours
Preparation12.00
Lectures12.00
Class Instruction9.00
Theoretical exercises35.00
Sum68.00
Aim and content
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 digitalization (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, environmental science, pharmaceutical science etc.

Methods for exploratory analysis (Principal Component Analysis), multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. The mathematics behind most of the concepts will be given together with the practical applications and considerations of the methods.

Even more important, though, is the understanding and interpretation of the computed models. As is methods for outlier detection and model validation. Computer exercises and cases will be performed applying user-friendly software. A thorough introduction to the software will be given.

Course content:

• Introduction to Multivariate Data Analysis
• Principal Component Analysis (PCA)
• Pre-processing
• Outlier detection
• Partial Least Squares Regression (PLSR)
• Validation
• Variable selection

Formal requirements
Basic statistical knowledge.

Learning outcome
Knowledge:
• 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
• Understand the basics of the algorithms behind the PCA and PLS
• Understand the math of data pre-processing

Skills:
• Apply theory on real life data analytical cases
• Apply commercial software for data analysis
• Interpret multivariate models (both exploratory and regression)

Competences:
• Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society

Target group
PhD students from any scientific field that gather data with several samples (+10) and many variables (+10).

Teaching and learning methods
There will be a mixture of several different teaching methodologies:
- Lectures (most also available as videos)
- Exercises + Walkthrough
- Short cases + Fish tank
- Day cases + Debriefing sessions
- Visual examples

Remarks
Responsible for scientific course content: Åsmund Rinnan, aar@food.ku.dk.

Collaborating departments at University of Copenhagen: PLEN and CHEM.

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