ST808: Multivariate Data Analysis and Chemometrics
Study Board of Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N370000102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Autumn
Level: Master's level course approved as PhD course
STADS ID (UVA): N370000101
ECTS value: 5
Date of Approval: 25-04-2019
Duration: 1 semester
Version: Approved - active
Entry requirements
Academic preconditions
Course introduction
The aim of the course is to enable the student to study multivariate calibration techniques and their applications in chemometrics.
The course builds on the knowledge acquired in the courses linear algebra and mathematical statistics.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- Give the competence to plan and execute scientific projects at a high level, including the management of work and development situations that are complex, unpredictable and that require new problem solving skills
- Give skills to master computer calculations
- Give knowledge about advanced models and methods in applied mathematics, based on international research, and knowledge about application of these models and methods on problems from various disciplines and from the private sector.
Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:
- describe the main chemometric methods for multivariate calibration and to know how to apply these in a specific context.
- know the application of a statistical software package, such as R, for solving concrete multivariate calibration problems, and to be able to describe the result of such an analysis in the form of a report.
- describe the advantages and disadvantages of different chemometric methods, in order to choose the correct method to solve a given multivariate calibration problem.
- describe the main methods for validation and optimization of a given calibration method for a specific problem, in order to assess the correctness of the method in the given context.
Content
The following main topics are contained in the course:
- Repetition of basic concepts from statistics and matrix algebra.
- Introduction to chemometrics and multivariate calibration.
- Multiple linear regression analysis (MLR).
- The classical least squares method (CLS).
- Principal components analysis (PCA).
- Principal components regression (PCR).
- Partial least squares regression (PLS).
- Validation and optimization of calibration model.
Literature
Examination regulations
Exam element a)
Timing
Autumn
Tests
Project report
EKA
N370000102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
5
Additional information
Evaluation is based on three project reports regarding chemometric data analyses set during the course
Indicative number of lessons
Teaching Method
In order to enable students to achieve the learning objectives for the course, the teaching is organised in such a way that there arexx8 lectures, class lessons, etc. on a semester.
These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:
- Intro phase (lectures) - 24 hours
- Training phase: 24 hours
Activities during the study phase: To study the course material and familiarise oneself with the statistical analyses in the R software package, individually or through group work.
Teacher responsible
Additional teachers
| Name | Department | City | |
|---|---|---|---|
| Nicky Cordua Mattsson | mattsson@imada.sdu.dk | Institut for Matematik og Datalogi |