Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Umweltnaturwissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2016)
Naturwissenschaftliche und technische Wahlfächer
Methoden der statistischen Datenanalyse
NummerTitelTypECTSUmfangDozierende
701-0104-00LStatistical Modelling of Spatial DataW3 KP2GA. J. Papritz
KurzbeschreibungIn 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.
LernzielThe 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.
InhaltAfter 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.
SkriptSlides, descriptions of the problems for the data analyses and solutions to them will be provided.
LiteraturP.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 / BesonderesFamiliarity 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.
252-0842-00LProgrammieren und Problemlösen Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 80
W3 KP2V + 1UD. Komm
KurzbeschreibungInformatikkonzepte und deren Umsetzung in Python.
LernzielDie Ziele der Lehrveranstaltung sind einerseits das Programmieren in Python zu vertiefen und andererseits Informatikkonzepte kennenzulernen, die im Algorithmendesign Anwendung finden. Hierbei liegt der Fokus auf dem algorithmischen Denken, also der Fähigkeit, Probleme systematisch mit Hilfe von entwickelten Algorithmen zu lösen. Es werden verschiedene Strategien für das Problemlösen vorgestellt, theoretisch analysiert und praktisch in Python umgesetzt. Die Verknüpfung von Theorie und Praxis ist in dieser Lehrveranstaltung zentral.
Inhalt- Repetition von grundlegenden Programmierkonzepten wie Variablen, Listen, Kontrollstrukturen und Schleifen
- Einlesen und darstellen von Daten
- Komplexitätstheorie
- Sortieren und Suchen
- Dynamische Programmierung
- Rekursion
- Graph-Algorithmen
SkriptVorlesungswebseite: Link
Voraussetzungen / BesonderesEmpfehlung:
- Grundlagen der Informatik (252-0852-00)
- Anwendungsnahes Programmieren mit Python (252-0840-01)
401-0102-00LApplied Multivariate StatisticsW5 KP2V + 1UF. Sigrist
KurzbeschreibungMultivariate 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.
LernzielAfter 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
InhaltVisualization, multivariate outliers, the multivariate normal distribution, dimension reduction, principal component analysis, multidimensional scaling, factor analysis, cluster analysis, classification, multivariate tests and multiple testing
SkriptNone
Literatur1) "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 / BesonderesThis 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.
401-6624-11LApplied Time SeriesW5 KP2V + 1UM. Dettling
KurzbeschreibungThe 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.
LernzielGetting 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.
InhaltThe 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.
SkriptA script will be available.
Voraussetzungen / BesonderesThe 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.
  •  Seite  1  von  1