The ECMI Educational Committee in response to the growing interest and development of methods related to data analysis, data science, data mining, and machine learning recommends the following educational programme as a model master programme that fulfils the following main mission statement of ECMI:
- To promote the use of mathematical modelling, simulation, and optimization in industry.
- To educate industrial mathematicians to meet the growing demand for such experts.
- To operate on a European scale.
The construction of this model master and its prerequisites is based on the 3+2 Bologna scheme. However, even though some flexibility is accepted, depending on local or national regulations, time has shown that master model curricula are still very diverse, even if only at the European level.
Block A: Recommended prerequisites to enter the master programme (two areas can be completed within the master programme, summing at most 12 ECTS):
- Basic knowledge in calculus – 12 ECTS
- Linear algebra – 6 ECTS
- Basics in numerical analysis – 6 ECTS
- Programming skills – 12 ECTS
- Basics in statistics and probability – 6 ECTS
Block B: Recommended courses/topics during the master programme (at least 6 of the topics and of at least 50% of the total number of ECTS of the programme must be completed during the master programme):
- Advanced programming techniques
- Advanced methods in statistics
- Big data and data mining techniques
- Graph theory
- Machine learning
- Optimization theory
- Risk management, extreme risk theory
- Stochastic processes including diffusion processes
- Unstructured data analysis (including natural language processing or image analysis)
Modelling activities: (at least 6 ECTS)
- Regular modelling seminar – at least 6 ECTS
2. Other modelling activities (e.g. summer schools, industrial projects, study groups, internships, etc.) – 0-9 ECTS
3. ECMI Modelling Week – 0-3 ECTS
Master thesis: A master thesis project of at least 25% of the total number of ECTS of the program. The thesis should be related to a real industrial problem (*). It could preferably be carried out in an interdisciplinary environment involving participants from industry and must have a data-driven problem.
ECMI Educational Committee emphasises that all courses offered within the topics in Block B should include mathematical aspects. Moreover, it is highly recommended to address the problem of ethical aspects in data science within the existing courses or within one separate course.
(*) A problem or application of mathematics is considered “Industrial” if (i) it has any societal, economical or technological impact; (ii) it is based on real or realistic data.