This course efficiently introduces the essential data science skills needed to develop and use adequate prediction models for quantitative data-based decision making.
On the one hand, the principles of commonly used methods for regression, classification and detection of data clusters are discussed, with special attention to the consequences of big data aspects. On the other hand, practical examples will be used to illustrate how models can be validated, compared and used.
Unique is that during the course participants gain experience in the visual programming of data workflows, with which model-fit, -validation and -comparison can be easily executed in practice.
After completion of this course:
Professionals who are involved in the analysis of quantitative data and the use of decision support systems, and managers who want to be able to assess and compare the quality of developed models or who control and steer these processes. The course is also suitable for lecturers at universities or colleges of higher education who want to be informed about developments in the field of data science, data mining and data analytics.
In consultation with the participants this course can be taught in Dutch or English.
|Trainer:||Dhr. Dr. J.J.M. Rijpkema (Eindhoven University of Technology (TU/e))|
|Course data:||October 1 and 8 - 2019|
|Price:||€ 1,190.00 ex. vat|
|In cooperation with:||TU/e, department of Mathematics & Computer Science|
|The program can be taught in English on request.|
During the course, practical examples and assignments are discussed. Participants gain experience with the use of the freely available source software Orange for visually programming data analysis workflows and interactively validating and comparing models.