Description: Modern laboratory equipment produces huge amounts of experimental data — spectral vectors with hundreds of wavelengths, microarrays, gene expression data, multi-channel images and many others. Even conventional measurements may end up with tens of variables. Such data represent a wealth of potential information but usually only a part of it relates to a problem of interest. This course teaches how to extract problem-dependent information from multivariate data. The practical part of the course assuming using R for calculations and visualization of results.
This course is split in two parts: first part (2 days, 1 ECTS) gives an introduction to descriptive and inferential statistics and R programming. The second part (3 days, 2 ECTS) is mainly devoted to analysis of multivariate data, including exploratory analysis, regression and validation, preprocessing and variable selection and pattern recognition.
In each part lectures are supplemented with a suite of real life examples and exercises as well as assignments, with which students will try the discussed methods by solving various data analysis problems.
Organizer: Associate Professor Sergey Kucheryavskiy, E-mail firstname.lastname@example.org
Lecturers: Associate Professor Sergey Kucheryavskiy
ECTS: 3 (1 + 2)
Time: 7-11 April, 2014
Place: Section of Chemical Engineering, Aalborg University, campus Esbjerg, Niels Bohrs vej, 8
Zip code: 6700
Number of seats:
Deadline: 17 March, 2014
- Teacher: Sergey Kucheryavskiy