Prices

ICES uses graded registration fees depending on the main employment of the participant.

Course material and books can not be bought separately from registration for the connected module.

==> Calculate the correct registration fee <==.

The following price categories are used:

AUGent: internal registration

AUGent-participants who pay via SAP have the advantage of internal rates.

AUGent staff and AUGent Ph.D-students who do not pay via SAP fall under the categories 'Participants from the non-profit sector' and 'Ph.D-students outside AUGent' as mentioned below.

 

Participants from the private sector

If three or more employees from the same company enrol simultaneously for a module an overall reduction of 10% is granted on the fee (excluding books).

FeesModuleBookExam
M1: Introduction to R 325 30 n/a
M2: Introductory Statistics 800 80 30
M3: Introduction to Statistics with Python 410 60 n/a
M4: Analysis of Variance 800 - 30
M5: Applied Linear Regression 800 - 30
M6: R Intermediate 325

30

n/a
M7: Experimental Design 900 B1: 75 / B2: 60 30
M8: Data Mining 900 n/a n/a
M9: Causal Inference 900 n/a 30

Participants from the non-profit and civil service

These prices also apply to AUGent staff who register without an SAP internal order.

FeesModuleBookExam
M1: Introduction to R 175 30 n/a
M2: Introductory Statistics 360 80 30
M3: Introduction to Statistics with Python 185 60 n/a
M3: Analysis of Variance 360 - 30
M4: Applied Linear Regression 360 - 30
M6: R Intermediate 175 30 n/a
M7: Experimental Design 405 B1: 75 / B2: 60 30
M8: Data Mining 405 n/a 30
M9: Causal Inference 405 n/a 30

(PhD)-students outside AUGent

These prices also apply to AUGent Ph.D-students who register without an SAP internal order.

FeesModuleBookExam
M1: Introduction to R 125 30 n/a
M2: Introductory Statistics 280 80 30
M3: Introduction to Statistics with Python 145 60 n/a
M4: Analysis of Variance 280 - 30
M5: Applied Linear Regression 280 - 30
M6: R Intermediate 125 30 n/a
M7: Experimental Design 315 B1: 75 / B2: 60 30
M8: Data Mining 315 n/a 30
M9: Causal Inference 315 n/a 30