High Dimensional Data Analysis-M9

Modern high throughput technologies easily generate data on thousands of variables; e.g. health care data, genomics, chemometrics, environmental monitoring, web logs, movie ratings, … 

Conventional statistical methods are no longer suited for effectively analysing such high-dimensional data. 
Multivariate statistical methods may be used, but for often the dimensionality of the data set is much larger than the number of (biological) samples. Modern advances in statistical data analyses allow for the appropriate analysis of such data.

Methods for the analysis of high dimensional data rely heavily on multivariate statistical methods. Therefore a large part of the course content is devoted to multivariate methods, but with a focus on high dimensional settings and issues.

Multivariate statistical analysis covers many methods. In this course a selection of techniques is covered based on our experience that they are frequently used in industry and research institutes.

The course is taught using case studies with applications from different fields (analytical chemistry, ecology, biotechnology, genomics, …).

Target audience

This course targets professionals and investigators from all areas that are high-dimensional.

Course prerequisites

Course prerequisites are ready at hand knowledge of basic statistics: data exploration and descriptive statistics, statistical modeling, and inference: linear models, confidence intervals, t-tests, F-tests, anova, chi-squared test, such as covered in 

Module 4 - Drawing Conclusions from Data: an Introduction,
Module 7 - Exploiting Sources of Variation in your Data: the ANOVA Approach 
Module 10 - Explaining and Predicting Outcomes with Linear Regression of this years' course program.

Exam / Certificate

There is no exam connected to this module. If you attend all classes you will receive a certificate of attendance via e-mail at the end of the course.

Schedule

Tuesday January 2025: 14, 21, 28  from 5.30 pm to 9.30 pm. Classroom 3.2

Thursday January 2025: 16, 23, 30 from 5.30 pm to 9.30 pm. Classroom 3.3

Venue

Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent, Building S1, 3th Floor

Fees

The participation fee is 1320 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations.

• Industry, private sector, profession*: € 1320
• Non profit, government, higher education staff: € 990
• (Doctoral) students, unemployed: € 595 | Full reimbursement by Doctoral Schools is possible (see details below).

*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.

Registration

To register, add the course below to your shopping cart and proceed to checkout.

Is this your first registration for a Beta Academy course? In that case, you will need to create an account first. Afterward, you will receive a confirmation email to activate your account on the academy platform. You do not have to click on the activation link but can immediately return to your shopping cart to complete your course registration. If you do not receive a confirmation email for your course order, please contact our Science Academy at science-academy@ugent.be.

Are you currently on the Nova-academy website? To proceed with the registration, simply click on the "More information" box located on the left side.

UGent PhD students

Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents. 

By registering for a course or event organized by the Science Academy, you agree to the cancellation procedure that you can find on our website

KMO-portefeuille

Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo

Organisation

Science Academy (IPVW)

Faculty of Science

science-academy@ugent.be

Website