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.

  • Overview of the courses

     

    Instructor

    Course

    Software

    Level

    Date

    ECTS

    Christian Kauth

    Introduction to Python forPredictive Modeling

    Python

    introductory

    Feb 8

     0.5

    Christian Kauth

    Unboxing the Machine Learning

    Algorithms in Python

    Python

    advanced

    Feb 9

     0.5

    Christian Kauth

    Deep Learning with Python

    Python

    advanced

    Feb 10

     0.5

    Martin Huber

    Data Analytics and Machine

    Learning with Applications in KNIME

    KNIME

    introductory (no programming required)

    Feb 11

     0.5

    Daniel Wegmann

    Introduction to R

    R

    introductory

    Feb 12

     0.5

    Helge Liebert

    Text analysis in R

    R

    advanced

    Feb 15-17

     1.5

    Martin Huber

    Predictive and Causal Machine

    Learning in R

    R

    advanced

    Feb 18-19

     1.0

     

     

  • 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.

  • Language

    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).

  • Credits

    Successful participation in the two -week course (and examination) is credited with 5 ECTS points. 

  • 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: 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.

     

    Other costs

    • travel costs to and from the course location
    • accommodation
    • meals
    • transportation within Fribourg
    • visa
    • health insurance
    • personal expenses during stay in Fribourg
  • 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.

  • Location

    University of Fribourg, Pérolles II, Boulevard de Pérolles 90, 1700 Fribourg, Switzerland.

    How to arrive