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 Scheda Riassuntiva
 Anno Accademico 2018/2019 Scuola Scuola di Ingegneria Industriale e dell'Informazione Insegnamento 098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES 098515 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES [2] Docente Vantini Simone Cfu 4.00 Tipo insegnamento Modulo Di Corso Strutturato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing - Civ (Mag.)(ord. 270) - MI (489) INGEGNERIA PER L'AMBIENTE E IL TERRITORIO - ENVIRONMENTAL AND LAND PLANNING ENGINEERING*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA*AZZZZ098516 - NUMERICAL AND STATISTICAL METHODS IN GEOSCIENCES

 Obiettivi dell'insegnamento
 The course aims at providing students with statistical tools for the analysis of data typically encountered in geo-science and environmental applications. The course is organized in theoretical classes and laboratories.

 Risultati di apprendimento attesi
 The students with respect to 1) Knowledge and understanding are expected to:  - know the fundamental principles of the mathematical and statistical methods for the analysis of non-standard data possibly with spatial and/or temporal dependence that are typically encountered in geo-science and environmental applications; - use proper terminology. 2)  Applying knowledge and understanding are expected to: - apply the acquired knowledge to engineering problems; - be able to provide an abstract formalization of the phenomena of interest; - use the proposed software to perform statistical data analysis.

 Argomenti trattati
 Euclidean Multivariate Data (brief review). The Euclidean geometry in the real space, Principal Component Analysis, clustering, permutational one- and two-population tests. Compositional Data (e.g., chemical compounds, mineralogical compositions, atmospheric pollutants). The Aitchinson geometry in the simplex, transformations of compositional data, Principal Component Analysis of compositional data, clustering of compositional data, permutational one- and two-population tests for compositional data. Directional Data (e.g, winds, waves, geological fault directions).The geodesic distance, Principal Component Analysis of directional data, clustering of directional data, permutational one- and two-population tests for directional data. Tensor Data (e.g., diffusion of oil, water, vehicles and people). Distances between tensors, clustering of tensor data, permutational one- and two-population tests for tensor data. Network Data (e.g., river networks, oil and gas pipelines, mobility networks). Network representations (adjacency, Laplacian, and modularity matrix), distances between networks, clustering of network data, permutational one- and two-population tests for network data. Data with Spatial and/or Temporal Dependence. Measures of spatial dependence, covariogram and variogram, estimation and prediction via ordinary and universal Kriging, hidden Markov random fields.  Course Organization The course is made of theoretical lectures (24 hours) followed by lab sessions (16 hours). During the theoretical lectures methods and algorithms will be presented in the proper mathematical framework. During the lab sessions methods and algorithms will be instead illustrated and tested through applications to real data sets. The analyses performed during the lab sessions will be carried out by means the opensource software R (www.r-project.org). Along the course, students are expected to work on a data analysis team project.

 Prerequisiti
 Common prerequisites: basic calculus and linear algebra. Statistical part: basic knowledge in probability and statistics at bachelor level is suggested.

 Modalità di valutazione
 Concerning the statistical part of the course, the students will be divided into teams and the examination will consist in: 1) reading, understanding and presenting a research paper assigned by the teacher (50% of the part-specific mark); 2) a team project on specific real world problem assigned by the teacher (50% of the part-specific mark). The students with respect to 1) Knowledge and understanding are expected to show that they:  - know the fundamental principles of the mathematical and statistical methods for the analysis of non-standard data possibly with spatial and/or temporal dependence that are typically encountered in geo-science and environmental applications; - are able to use proper terminology. 2)  Applying knowledge and understanding are expected to show that they: - are able to apply the acquired knowledge to engineering problems; - are able be able to provide an abstract formalization of the phenomena of interest; - are able to use the proposed software to perform statistical data analysis.

 Bibliografia
 V. Pawlowsky-Glahn and A. Buccianti, Compositional data analysis. Theory and applications. , Editore: John Wiley and Sons Ltd., Anno edizione: 2011 K. V. Mardia and P. Jupp, Directional Statistics , Editore: John Wiley and Sons Ltd., Anno edizione: 2000 P. I. Good, Permutation, Parametric and Bootstrap Tests of Hypotheses, Editore: Springer, New York, Anno edizione: 2007 E. D. Kolaczyk, Statistical Analysis of Network Data, Editore: Springer, Anno edizione: 2009 N. Cressie, Statistics for Spatial data, Editore: John Wiley & Sons, New York, Anno edizione: 1993

 Software utilizzato
 Nessun software richiesto

 Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
24:00
36:00
Esercitazione
16:00
24:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 40:00 60:00

 Informazioni in lingua inglese a supporto dell'internazionalizzazione
 Insegnamento erogato in lingua Inglese Disponibilità di materiale didattico/slides in lingua inglese Disponibilità di libri di testo/bibliografia in lingua inglese Possibilità di sostenere l'esame in lingua inglese Disponibilità di supporto didattico in lingua inglese
 schedaincarico v. 1.7.2 / 1.7.2 Area Servizi ICT 05/07/2022