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 one- to three-days-coursescover 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.
The working language will be English. The courses will take place in spacious lecture halls at the Pérolles Campus (Boulevard de Pérolles 90, CH-1700 Fribourg) to meet COVID-19 distancing requirements.
Participants are free to attend all courses or to select a subset of courses offered in the winter school.
|When?||07.02.2022 09:00 - 18.02.2022|
Bd de Pérolles 90
|Contact||Chair of Applied Econometrics - Department of Economics, Faculty of Management, Economics and Social Sciences
Bd de Pérolles 90
+41 26 300 82 55 90
Online registration deadline: Jan 31st 2022