Winter School in Data Analytics and Machine Learning

Colloque / Congrès / Forum
Académique ou spécialiste
06.02.2023 08:30    -    17.02.2023 16:30

Many firms and organizations have recognized the value of analyzing data based on quantitative tools like regression, machine learning, and deep learning 

- for forecasting specific outcomes such as sales or prices (predictive analysis),
- for evaluating the causal impact of specific actions such as offering discounts or running marketing campaigns (causal analysis). 

This permits improving the quality of decision making and thus increasing efficiency and competitiveness. 

The “Fribourg Winter School in Data Analytics and Machine Learning” provides training in state-of-the-art quantitative tools for predictive and causal analysis. The winter school takes place in hybrid form, implying that participants can attend courses either in class (face-to-face) or online. Please note that the sessions will not be recorded. The one- to three-days-courses cover both introductory and more advanced topics, using the open source software packages “Python”, “R”, and “Knime”. “Python” and “R” are among the most popular programming languages in data science and statistics, while “Knime” is a user-friendly, flow-chart based graphical interface that does not require any programming skills.

Among the topics covered in the various courses are

- regression techniques for multivariate statistical analysis;
- machine and deep learning algorithms like lasso, decision trees, random forests, and neural nets;
- text analysis to extract and statistically analyze text information from websites, like sentiments about products.
06.02.2023 08:30    -    17.02.2023 16:30
Site PER 21
Bd de Pérolles 90, 1700 Fribourg
Chair of Applied Econometrics - Department of Economics, Faculty of Management, Economics and Social Sciences
Lötscher Karin
Bd de Pérolles 90
1700 Fribourg
+41 26 300 8255
Madleina Caduff
Martin Huber
Christian Kauth
Helge Liebert
Inscription Obligatoire

Karin Lötscher; karin.loetscher@unifr.ch

Date limite: 31.01.2023


Pièces jointes