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.
Due to the current Covid-19 situation, the winter school will take place fully online (rather than in presence in Fribourg) via “Microsoft Teams”. For this reason, participants will be provided with a Microsoft teams link for attending their chosen courses.
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 courses (and examinations) can be credited with up to 5 ECTS points (if winter schools are recognized by the home university/institution)
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.
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.