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Publication type: Pedagogic
Author: Rui A. Pita Perdigão (R.A.P. Perdigão)
Title: Interdisciplinary Data Analytics and Model Design
Indexed: Yes (Crossref)
Cite as: Perdigão, Rui A. P. (2020): Interdisciplinary Data Analytics and Model Design. https://doi.org/10.46337/mdsc.5455.
Methodological Keywords: Information Physics, Information Theory, Complex Systems, Dynamical Systems, Mathematical Physics, Non-Ergodic, Chaos, Entropy, Emergence, Synergy, Coevolution, Quantum Information, Post-Quantum Theory.
Applied Keywords: Big Data Analytics, Artificial Intelligence, Machine Learning, Earth System Dynamics, Socio-Environmental Systems, Climate Change, Sustainability.
Rui Perdigão’s doctoral course on Interdisciplinary Data Analytics and Model Design is headquartered and primarily delivered at the Meteoceanics Institute for Complex System Science and partner institutions.
Moreover, since 2019 it is also available as a semester doctoral course at the University of Lisbon (offering 6 ECTS for students enrolled in partner programs). The course is specially tailored to a wide-spectrum interdisciplinary audience spanning across natural, social, technical and exact sciences.
The associated pedagogic materials emerge as a natural guide for graduate students, researchers and practitioners alike, matching the structure of the course as follows:
- 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.
Queries regarding this contribution can be addressed to the Fluid Dynamical Systems Contact Form, or mdsc[at]meteoceanics.eu.