Multivariate Data Analysis and Experimental Design

5 credits

Syllabus, Bachelor's level, 1MB344

A revised version of the syllabus is available.
Code
1MB344
Education cycle
First cycle
Main field(s) of study and in-depth level
Computer Science G2F, Technology G2F
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 27 April 2010
Responsible department
Biology Education Centre

Learning outcomes

After successful completion of the course the student should be able to

  • describe the theoretical basis for and being able to use some of the basic methods for exploratory multivariate data analysis: data compression and visualisation.
  • describe the theoretical basis for and being able to use some of the basic methods for predictive multivariate data analyses: multidimensional regression and classification.
  • interpret the results obtained using the methods referred to above.
  • use experimental design methods and adjust experiments and methodologies in accordance with the resources available.
  • carry out the analyses and interpretations using at leas one kind of general software environment for analysis, for example MATLAB or R.

Content

Exploratory multivariate data analysis: pre-processing, principal component analysis, clustering etc. Predictive multivariate data analyses: model based and model-free linear/nonlinear classification and regression, model selection and performance estimation. Applications.

Instruction

Lectures, seminars, exercises based on manual as well as computer calculations.

Assessment

Theory 3 (hp), Exercises (2 credits).

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