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.

• Overview of the courses

  Instructor	Course	Software	Level	Date	ECTS
  Martin Huber	Data Analytics and Machine Learning with Applications in KNIME	KNIME	introductory (no programming required)	Feb 7	0.5
  Christian Kauth	Introduction to Python for Predictive Modeling	Python	introductory	Feb 8	0.5
  Christian Kauth	Unboxing the Machine Learning Algorithms in Python	Python	intermediate	Feb 9	0.5
  Christian Kauth	Deep Learning with Python	Python	advanced	Feb 10	0.5
  Daniel Wegmann	Introduction to R	R	introductory	Feb 11	0.5
  Martin Huber	Predictive and Causal Machine Learning in R	R	intermediate	Feb 14-15	1.0
  Helge Liebert	Text analysis in R	R	advanced	Feb 16-18	1.5

• Participants

  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.

• Teaching modalities

  The working language will be English. Courses are held in a spacious lecture hall (A230, 2nd floor) on the Pérolles campus (Boulevard de Pérolles 90, CH-1700 Fribourg) to meet COVID-19 requirements. 

• 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).

• Credits

  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)

• Course fees

  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: Knime course (7 Feb) CHF 200	Block 1: Knime course (7 Feb) CHF 300
  	Block 2: Python courses (8-10 Feb) CHF 500	Block 2: Python courses (8-10 Feb) CHF 800
  	Block 3: R courses (11-18 Feb) CHF 600	Block 3: R courses (11-18 Feb) CHF 900
  	All courses: (7-18 Feb) CHF 950	All courses: (7-18 Feb) CHF 1500

  *Please note: Not all courses have to be attended.  

   

• Payment

  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, 2022. 

• Location

  University of Fribourg, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland. Room A230 (section A, 2nd floor).

  How to arrive

• Accomodation

  Participants must book their accommodations themselves.

  Here are a few recommendations:

  Convid Salesianum

  Hotel aux Remparts

  Hotel du Faucon

  Hotel de la Rose