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Tartu university in the frame of Nordic-Baltic-Russian Network in Bioinformatics invites to the master degree course "Biological Data Analysis". Baltic, Nordic and Russian students are welcome to apply to the course with deadline of November 20, 2006. More detailed description can be found in the course page. Do not miss your chance to take advantageous course in Estonia!

Course is free of any participation fees and all basic students' expenses will be covered by Bioinformatics network. Nordic Council of Ministers providers a support to Bioinformatics network in the frame of NordPlus Neighbour program.

Students willing to join the course shall fill application form and traditionally contact local BOVA coordinator at home university for support arranging travel.  Following agreement between BOVA member universities credit points received from the course may be accepted to your curricula depending on your study program. In all cases, course will be reordered in your diploma supplement including the statement on your international experiences.

Brief course description

The "Biological Data Analysis" course is the second of the 3 planned courses in Bioinformatics field. The students are strongly encouraged (but not obligatory) to plan to participate in all 3 courses.

The aim of the "Biological Data Analysis" course is to provide students with basic knowledge and elementary skills needed for the statistical analysis of biological data.

In the preparatory, distance-learning part of the course, students will be asked to download the freeware package R and to practice simple numerical and graphical tools for data description using this software. R is very flexible software for biological data analysis; the number of implemented methods is virtually unlimited (although only a small subset of them will be covered during this course). The same software and its extensions will also be used in the next, bioinformatics course.

The one-week face-to-face module will be held in Tartu, using the facilities of the Faculty of Mathematics and Computer Sciences, University of Tartu. First, the concepts of randomness, random sampling and sampling variability will be introduced. Drawing conclusions from the data, while taking into account uncertainty, is based on confidence intervals and statistical hypothesis testing. Their general principles will be seen together with practical examples of data analysis in R. Next, some methods for exploring and testing associations in the data will be considered: simple two-sample tests, basic statistical models, such as (simple and multivariate) linear regression and analysis of variance. Finally, some tools for dimensionality reduction and cluster analysis will be explored. The practical exercises sessions will be held in a computer lab.

Prepared on the base of information submitted by
Prof. Irina Arhipova
Latvia University of Agriculture

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