General Information
Course Code | X_418156 |
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Credits | 6 EC |
Period | P5 |
Course Level | 400 |
Language of Tuition | English |
Faculty | Faculty of Science |
Course Coordinator | dr. D. Molenaar |
Examiner | dr. D. Molenaar |
Teaching Staff |
dr. D. Molenaar |
Practical Information
You need to register for this course yourself
Last-minute registration is available for this course.
Teaching Methods | Lecture, Computer lab |
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Target audiences
This course is also available as:
Course Objective
Biologists often have to handle, analyze, and present analysis resultsof large sets of biological data, originating from genomics,
transcriptomics, proteomics, and metabolomics experiments. In many
cases, these tasks cannot be performed using standard “press the button”
commercial statistical packages. A popular solution to this problem is
the use of the open source statistical programming environment R. R is
used intensively in the community of experimental biologists, and most
newly published data analysis techniques are first available as
R-packages.
Goals:
- To obtain knowledge of the structure of the R-language as well as
practical skills in the programming, producing graphs and documents with
R
- To obtain hands on experience in handling and investigation of data
sets
- To obtain an overview of some modern statistical learning techniques
Course Content
This course focuses on obtaining the practical skills to perform datahandling and analysis tasks from small to large data sets, and to
graphically display and interpret the results. Statistical analyses will
center on multivariate analysis. Examples of the generation and
interpretation of (large) data sets will be presented from the various
fields of biology, ranging from cell biology to ecology. The items
treated during the lectures will be studied in computer practical
sessions using R.
Teaching Methods
Lectures, tutorials, computer practical work, self-studyMethod of Assessment
Assessment will take place by three individual assignments.Literature
A course syllabus with theory and computer exercises will be madeavailable
Target Audience
Master students having a background in basic statisticsRecommended background knowledge
Knowledge of basic statistics and some experience with a programminglanguage