Welcome to Introduction to Multivariate Data Analysis
Description: Modern laboratory equipment produces huge amounts of experimental data — spectral vectors with hundreds of wavelengths, microarrays, gene expression data, sensors, 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, 3 ECTS) is mainly devoted to analysis of multivariate data, including exploratory analysis, regression and validation, preprocessing and variable selection as well as classification.
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. To complete the course, participants have to work on three mini-projects and submit their results in form of reports within 2-3 month after the main part if finished.
Organizer and lecturer: Associate Professor Sergey Kucheryavskiy, e-mail: email@example.com
ECTS: 4 (1+3)
Time: 28 May to 1 June, 2018
Place: Section of Chemical Engineering, Aalborg University, campus Esbjerg, Niels Bohrs vej, 8
Zip code: 6700
City: Esbjerg, Denmark
Number of seats: 15 (at least 7-10 from AAU)
Deadline: 7 May 2018
Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
- Teacher: Sergey Kucheryavskiy