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 R)
• 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

Why learn to program?

• Solid hands-on work with data requires programming skills. It is only when you have done hands-on work with data yourself that you can relate to and adopt the perspective of a technical data scientist. Only when you are able to adopt this perspective, can you bridge between the world of management, on the one hand, and the world of technical data scientists, on the other.
• Steve Jobs said “Everybody in this country should learn to program a computer, because it teaches you how to think”. We believe that he has had a point in that adopting the logic of a computer program promotes your general problem-solving skills.

Program content

• Extensive core in data science, machine learning and programming
• Fundamentals of programming
• Data handling
• Statistical modelling
• Classification
• Regression
• Neural networks
• Forecasting

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

The program has a total of 24 ECTS and includes:

• Fall semester: a mandatory two-weeks workshop during the fall break and weekly 4-hour sessions before and after the fall break  (8 ECTS)
• Elective courses (12 ECTS).
• A mandatory course «Multidisciplinary Perspectives on Data Science» (4 ECTS) (spring semester).
  
  • This course counts towards your major as elective in the following programs:
    Electives: 19BBW:HS19, 18BBW:HS19, 19BVWL:HS19, 18BVWL:HS19, 18BIA:HS19
  • In BLE20, the course counts towards your major as a mandatory elective.
    

ECTS of courses count for your major (except for the workshop). For details check the fact sheets.