Interdisciplinary Data Analytics and Model Design at ICS-ULisboa

Rui Perdigão’s “Interdisciplinary Data Analytics and Model Design” is now also available as a semester doctoral course at the University of Lisbon, offering 6 ECTS for students enrolled in partner programs.

Objectives

  • 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.

Program

  • 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.

 

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