NSCPHD1012 Introduction to MATLAB for Multivariate Data Analysis

Volume 2015/2016


PLEASE NOTE         

The PhD course database is under construction. If you want to sign up for this course, please click on the link in order to be re-directed. Link: https://phdcourses.ku.dk/nat.aspx

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.

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.
  • Category
  • Hours
  • Exam
  • 1
  • Lectures
  • 15
  • Practical exercises
  • 20
  • Preparation
  • 25
  • Project work
  • 15
  • Total
  • 76
Type of assessment
Written assignment
Written report based on 5 short examination assignments; work 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 lectures.
Marking scale
passed/not passed
Censorship form
No external censorship