Professor Rui Perdigão’s “Interdisciplinary Data Analytics and Model Design”, anchored on the Meteoceanics Institute for Complex System Science, is now also available as a semester doctoral course at the Interuniversity Institute for Intelligence, Complexity and Predictability, offering 6 ECTS for students enrolled in programs from partner universities.
- Acquisition of fundamental competences in data analysis, its relevance and implementation in the conceptualization and formal analysis of systems in an interdisciplinary perspective;
- Learning fundamental techniques for information retrieval, analysis and treatment along with its uncertainties, from data acquisition to model design;
- Acquisition of new competences in scientific research, development and communication at the interface between natural and social sciences;
- Special emphasis on interdisciplinary challenges of climate change and decision support towards sustainable development.
- Beneath Data, there is a Story: Fundamental principles behind the nature, geometry and dynamics of information across natural, social and joint systems;
- Retrieving the Story: Fundamental methods for data analytics and model design. From spatiotemporal geostatistics to broader dynamic information tools for data mining, pattern recognition, causal analysis and model design;
- Quality-checking the Story: Techniques for quality check, uncertainty assessment and data processing towards strengthening information reliability;
- Sharing the Story: Techniques for data visualization, information sharing and overall communication of scientific results;
- GeoSys Operation: Operational real-world examples for a) data mining and machine learning in large satellite datasets; b) nonlinear analytics and model design for earth system dynamics; c) early warning and automated decision support systems in natural (e.g. hydro-meteorological, geophysical) hazards;
- Frontier Operation: early warning detection and adaptive decision support of critical transitions and extremes in the earth system under climate change;
- Hands-On: Simple analytical and computational examples on the prior points.