Winter School in Data Analytics and Machine Learning
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-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.
The courses will take place in spacious lecture halls at the Pérolles Campus to meet COVID-19 distancing requirements.
For those participants who cannot or do not want to come to the University of Fribourg due to Covid-19 restrictions, it will also be possible to follow the courses online live via «Microsoft Teams». Please contact Mrs. Sommer-Gauch for more information concerning this possibility.
Overview of the courses
introductory (no programming required)
The winter school is open to BA students, MA students, Ph.D. students, and academic researchers (such as post-docs and professors) at the University of Fribourg and at other universities or research institutions, as well as to employees of private companies and the public sector.
The working language will be English.
Examination / Evaluation
Take home exam: students obtain a dataset with several exercises to solve that need to be resubmitted in order to be graded. Without taking the exam, students can nevertheless obtain a certificate of participation (without grade).
Successful participation in the two -week course (and examination) is credited with 5 ECTS points.
BA, MA, and Ph.D. of the University of Fribourg
BA/MA/PhD students at other universities or academic researchers (e.g. post-docs, professors...) at the University of Fribourg or elsewhere
private companies and public sector
*Flat rate for all courses: CHF 90
Block 1: Python courses (8-10 Feb) CHF 500
Block 1: Python courses (8-10 Feb) CHF 800
Block 2: Knime course (11 Feb) CHF 200
Block 2: Knime course (11 Feb) CHF 300
Block 3: R courses (12-19 Feb) CHF 600
Block 3: R courses (12-19 Feb) CHF 900
All courses: (8-19 Feb) CHF 950
All courses: (8-19 Feb) CHF 1500
*Please note: Not all courses have to be attended.
The course fee includes tuition, course materials, access to the university library, and internet access.
Not covered by the course fee are the following costs:
Social events (NOTE: realization will depend on the COVID-19 situation)
- Wednesday 10 and 17 February: social dinner
- Thursday 9 and 16 February: night skiing
Important: If you are interested in participating in one or more events, please write an e-mail to andrea.sommer-gauch@unifr. We will then provide you with additional information.
- travel costs to and from the course location
- transportation within Fribourg
- health insurance
- personal expenses during stay in Fribourg
Payment is made directly via the online registration form. Please note that the registration is only complete when we have received the registration fee. The deadline for online registration and payment is January 31, 2021.