Suchergebnis: Katalogdaten im Frühjahrssemester 2019
Umweltnaturwissenschaften Bachelor | ||||||
Bachelor-Studium (Studienreglement 2016) | ||||||
Naturwissenschaftliche und technische Wahlfächer | ||||||
Methoden der statistischen Datenanalyse | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|
701-0104-00L | Statistical Modelling of Spatial Data | W | 3 KP | 2G | A. J. Papritz | |
Kurzbeschreibung | In environmental sciences one often deals with spatial data. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such analyses. | |||||
Lernziel | The course will provide an overview of the basic concepts and stochastic models that are used to model spatial data. In addition, participants will learn a number of geostatistical techniques and acquire familiarity with R software that is useful for analyzing spatial data. | |||||
Inhalt | After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by linear regression, modelling autocorrelation by variogram; kriging: mean square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves. | |||||
Skript | Slides, descriptions of the problems for the data analyses and solutions to them will be provided. | |||||
Literatur | P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer. Bivand, R. S., Pebesma, E. J. & Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R. Springer. | |||||
Voraussetzungen / Besonderes | Familiarity with linear regression analysis (e.g. equivalent to the first part of the course 401-0649-00L Applied Statistical Regression) and with the software R (e.g. 401-6215-00L Using R for Data Analysis and Graphics (Part I), 401-6217-00L Using R for Data Analysis and Graphics (Part II)) are required for attending the course. | |||||
252-0842-00L | Programmieren und Problemlösen Maximale Teilnehmerzahl: 40 | W | 3 KP | 2V + 0.5U | D. Komm | |
Kurzbeschreibung | Einführung in die Programmierung in Java und in das Problemlösen mittels Standard-Algorithmen und -Datenstrukturen. | |||||
Lernziel | Die Ziele der Lehrveranstaltung sind einerseits mit der Programmiersprache Java vertraut zu sein und andererseits gegebene Probleme des eigenen Fachbereichs (z.Bsp. Datenverarbeitung) mittels eigener Programme lösen zu können. Die Studierenden sollen bestehende Algorithmen und Datenstrukturen kennen, diese benutzen können und deren Eigenschaften kennen. Das Ziel ist es, für ein gegebenes Problem eine geeignete Datenstruktur und einen geeigneten Algorithmus auswählen zu können und das eigene Programm, basierend auf dieser Wahl, programmieren zu können. Während der Lehrveranstaltung arbeiten die Studierenden an einem eigenen Projekt, das sie während der letzten Vorlesungsstunde präsentieren müssen. | |||||
Inhalt | Folgende Themen werden behandelt: - Programmierkonzepte vs. Programmiersprachen - Einführung in Java - Arrays - Methoden und Methodenparameter - Klassen, Typen und Objekte - I/O: Tastatureingaben, Bildschrimausgaben, Dateien lesen und schreiben - Exceptions - Lambda Ausdrücke und das Stream API - Datenstrukturen - Einführung in GUI-Programmierung | |||||
Skript | Vorlesungswebseite: Link | |||||
Voraussetzungen / Besonderes | Achtung: Dies ist ein Blockkurs, der nur während der ersten sieben Wochen des Semesters stattfindet. Diese sieben Wochen sind sehr intensiv, da gleichzeitig das bewertete Projekt umgesetzt wird. Empfehlung: - Einsatz von Informatikmitteln (252-0839-00) - Anwendungsnahes Programmieren mit Python (252-0840-01) | |||||
401-0102-00L | Applied Multivariate Statistics | W | 5 KP | 2V + 1U | F. Sigrist | |
Kurzbeschreibung | Multivariate statistics analyzes data on several random variables simultaneously. This course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics including visualization, dimension reduction, supervised and unsupervised learning for multivariate data. An emphasis is on applications and solving problems with the statistical software R. | |||||
Lernziel | After the course, you are able to: - describe the various methods and the concepts behind them - identify adequate methods for a given statistical problem - use the statistical software R to efficiently apply these methods - interpret the output of these methods | |||||
Inhalt | Visualization, multivariate outliers, the multivariate normal distribution, dimension reduction, principal component analysis, multidimensional scaling, factor analysis, cluster analysis, classification, multivariate tests and multiple testing | |||||
Skript | None | |||||
Literatur | 1) "An Introduction to Applied Multivariate Analysis with R" (2011) by Everitt and Hothorn 2) "An Introduction to Statistical Learning: With Applications in R" (2013) by Gareth, Witten, Hastie and Tibshirani Electronic versions (pdf) of both books can be downloaded for free from the ETH library. | |||||
Voraussetzungen / Besonderes | This course is targeted at students with a non-math background. Requirements: ========== 1) Introductory course in statistics (min: t-test, regression; ideal: conditional probability, multiple regression) 2) Good understanding of R (if you don't know R, it is recommended that you study chapters 1,2,3,4, and 5 of "Introductory Statistics with R" from Peter Dalgaard, which is freely available online from the ETH library) An alternative course with more emphasis on theory is 401-6102-00L "Multivariate Statistics" (only every second year). 401-0102-00L and 401-6102-00L are mutually exclusive. You can register for only one of these two courses. | |||||
401-6624-11L | Applied Time Series | W | 5 KP | 2V + 1U | M. Dettling | |
Kurzbeschreibung | The course starts with an introduction to time series analysis (examples, goal, mathematical notation). In the following, descriptive techniques, modeling and prediction as well as advanced topics will be covered. | |||||
Lernziel | Getting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models, advanced methods and software that are necessary such that the student can independently run an applied time series analysis. | |||||
Inhalt | The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis. | |||||
Skript | A script will be available. | |||||
Voraussetzungen / Besonderes | The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis. |
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