Programme Structure

Programme Structure

  • A mandatory course “Workshop Fundamentals of Data Science” in the fall term (8 credits).
    • Weekly 4-hour sessions before and after the fall break and a mandatory two-week workshop during the fall break in the fall term.
  • A mandatory course “Multidisciplinary Perspectives on Data Science” in the spring term (4 credits).
    • This course counts towards your major as elective in the following programs:
      Electives in BBW, BVWL, BVWL, BIA
    • In BLE, the course counts towards your major as a mandatory elective.
  • Various elective courses (12 credits).
    • Credits from elective courses count for the DSF as well as for your major.

Learning goalsexpand_less

Learning goals: technical perspective

  • Fundamentals of programming (with a focus on data analysis and machine learning)
  • Data handling

Learning goals: analytical perspective

  • Preparation of data using popular open-source-programming environments (such as Python)
  • Statistical modelling, machine learning, artificial intelligence
  • Practical applications

Learning goals: decision maker’s perspective

  • Students that have acquired the DSF-Certificate:
  • Identify the potential of creating business and societal value out of data
  • Can communicate and work with “technical” and legal experts in a proactive and solution-oriented manner
  • Can explain the strengths or weaknesses of proposed solutions to those members of the management less well versed in technical matters
  • In general have a sound ability to reflect on the methods and phenomena of the digital age

Extensive focus on data science, machine learning and programming

  • Fundamentals of programming.
  • Data handling.
  • Statistical modelling.
  • Classification.
  • Regression.
  • Neural networks.
  • Forecasting.

Multidisciplinary perspectives on data science

  • Data-driven business strategies.
  • Ethical aspects.
  • Legal aspects.
  • Political aspects. 
  • Micro- and macroeconomic aspects.
3,580,1.00 Fall Semester: Workshop Fundamentals of Data Science
  • Handling of datasets using the Python programming language;
  • Preparing data for analysis
  • Producing graphs and using visualizing tools
  • Estimating statistical models
  • Assessing the performance of statistical models
  • Classification and regression models
  • Linear and logistic regression
  • Tree models (regression trees, random forests, boosting)
  • Neural networks


4,580,1.00 Spring Semester: Multidisciplinary perspectives on data science
  • Data-driven business strategies
  • Ethical aspects
  • Legal aspects
  • Political aspects
  • Micro- and macroeconomic aspects
  • Data science in insurance and finance

For the elective courses of the DSF programme, you can choose from the courses listed here.

Most courses offered in the Spring Term or Fall Term will be offered in future again; but we cannot guarantee the course implementation in future. New courses will be added on a rolling basis.

Participants starting the DSF program in Fall can choose courses offered in the same semester and later at their BA-level.

  • You are encouraged to make use of the various exchange programmes the university offers and it is possible to get credits for courses taken during your exchange.
  • Please inquire about crediting of non-HSG courses with the admissions office. You will need to provide information about the course (course fact sheet, syllabus, ECTS, etc.).
  • You may contact the DSF programme before chosing your courses so that we can pre-check whether your chosen courses can be credited towards the DSF certificate.
  • If you start the DSF programme in your 5th semester, no exchange is possible neither in your 5th nor in your 6th semester!

Both compulsary courses and the vast majority of the elective courses are taught in English


Johannes Binswanger

Prof. Dr.

Akademischer Programmleiter

Bodanstrasse 8
9000 St. Gallen

Sebastian Plappert


Administrative Programmleiter

Büro 52-5068
Müller-Friedberg-Strasse 6/8
9000 St. Gallen