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Introduction to MATLAB for Multivariate Data Analysis
Provider: Faculty of Science

Activity no.: 5308-18-04-31 
Enrollment deadline: 03/05/2018
Date and time23.04.2018, at: 09:00 - 03.05.2018, at: 16:00
Regular seats40
ECTS credits3.00
Contact personJose Manuel Amigo Rubio    E-mail address: jmar@food.ku.dk
Enrolment Handling/Course OrganiserJose Manuel Amigo Rubio    E-mail address: jmar@food.ku.dk
Written languageEnglish
Teaching languageEnglish
Block noteDuration: 6 day course, 6 contact hours a day. 23-26 April and 2-3 May 2018.
Scheme groupNot included in the scheme group
Scheme group noteThis course is a 6-day focused at PhD (candidate) level with an interest in using the programming and analysis software MATLAB (MATrix LABoratory) for general Data Analysis and Chemometric modeling.
Exam formWritten assignment
Exam detailsWritten report based on 5 short examination assignments worked out by the participants individually. The reports have to be handed in maximally one week after the last lecture and are evaluated and credited with PASS/FAIL by the course lecturers.
Grading scalePassed / Not passed
Course workload
Course workload categoryHours
Practical exercises20.00
Project work15.00


The course offers a platform for students and researchers to start handling and managing their scientific data. The course gives a first impression of the possibilities of MATLAB and its structure, data handling, plotting facilities, and the beginning of programming.

1- Introduction to MATLAB interface.
2- Array structures in MATLAB.
3- Basis of Chemometrics. PCA and PLS in MATLAB.
4- Scripts, functions and loops.
5- Plotting tools.
6- Tricks and useful stuff.
7- Exam.

Learning outcome

Knowledge, skills, competences:
- MATLAB interface: being able to “move around” in the most important utilities and windows in MATLAB
- Programming: being able to understand the structure of functions and to create small functions independently; to use loops and conditions in MATLAB programming.
- Data structure: being to identify and use different arrays structures of MATLAB and different ways of creating structures for data.
- Data handling: learn different ways of importing and handling data, searching tools and data managing.
- Chemometrics: being able to apply the basic Chemometric tools Principal Component Analysis and Partial Least Squares regression.
- Graphical representation: being able to use basic and advanced static and dynamic plots.


Handouts and scientific papers provided during the course; scripts and source code provided during the course.

Teaching and learning methods
Contact Teaching: the basis of the course teaching will be done by presentations. Lectures in power-point plus examples/exercises with the computer. The students will be able to follow all the exercises in their own computers. - Learning: Apart from the exercises presented at class, the students will be given several exercises that the must solve for the report. - Educational approaches: This course is addressed to PhD students or advanced Master students. The educational background of the students will be different.

IMPORTANT! After enrolling into the course, please send an e-mail to:

José Manual Amigo: jmar@food.ku.dk

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