Curriculum
The programme runs over two years and encompasses 120 credits, including a thesis.
First year of studies
Mandatory courses:
- Data mining and Statistical learning, 15 credits
- Data mining – clustering and association analysis, 15 credits
- Introduction to advanced academic studies, 3 credits
- Philosophy of science, 3 credits
Supporting courses:
- Linear statistical models, 12 credits
- Database technology, 6 credits
- R programming, 6 credits
- Probability and Statistics, 6 credits
Profile courses:
- Probability theory , 6 credits
- Theory of statistics, 6 credits
- Computational Statistics, 6 credits
- Neural networks and learning systems, 6 credits
- Multivariate statistical methods, 6 credits
- Web programming and interactivity, 6 credits
- Advanced web programming, 6 credits
Second year of studies
Mandatory courses:
- Bayesian learning, 6 credits
- Visualization, 6 credits
- Master’s thesis, 30 credits
Profile courses:
- Survey sampling, 6 credits
- Time series analysis, 6 credits
- Data mining project, 6 credits;
- Health statistics, 6 credits
- Text mining, 6 credits
- Optimization, 6 credits
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Last updated: Tue Oct 09 15:22:26 CEST 2012

