EventoWeb
Zürcher Hochschule für Angewandte Wissenschaften
Menu Home User Menu
Not registered Login
[ German (Switzerland) German (Switzerland) ]   [ English ]
[ de de ]   [ en ]
Not registered Login
n.BA.AD.AdES.24HS (Advanced Environmental Statistics) 
Module: Advanced Environmental Statistics
This information was generated on: 07 November 2025
No.
n.BA.AD.AdES.24HS
Title
Advanced Environmental Statistics
Credits
2

Description

Version: 3.0 start 01 August 2025

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
  Compulsory Module  X Elective Module    Optional Module
Planned Semester 5th Semester
Module Coordinator Jürgen Dengler
Telephone / E-Mail +41 (0)58 934 50 84 / juergen.dengler@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Jürgen Dengler and various internal lecturers
Entrance Requirements Inhalte aus den Modulen «Statistik und Wahrscheinlichkeit», «Statistische Modellierung und Simulation», «Applied Environmental Statistics» und «Environmental Systems 1»
Learning Outcomes and Competencies
Technical skills:
 
The students 
  • appreciate both the potential and the limitation of typical large datasets in ecology and environmental sciences and are able to adjust their methodology to this framework.
  • are able to apply appropriate advanced statistical methods to large datasets from different fields of ecology and environmental sciences.
  • are able to present and interpret the outputs of complex analyses appropriately.
     
Transferable skills:
 
The students use a wide range of sources to find solutions to statistical problems
Module Content
  • Retrieval and handling of large open access data that are useful in such statistical analyses
  • Proper handling of spatial autocorrelation and other “problems” in environmental datasets
  • Species distribution models (SDMs)
  • Analysis of movement and photo trap data of animals
  • Econometric modelling of the climate change impact on economic activities
  • Bayesian statistics
  • Outlook on some more advanced statistical techniques, such as regression tree techniques (e.g. BRTs), structural equation models (SEMs) and meta-analyses
Follow-up Modules “Computational Modelling in Environmental Sciences”, “Spatio-temporal Data Science”, “Probabilistic Modelling”
Methods of Instruction  Flipped classroom: 
  • The students will receive a detailed reader with theoretical background and information on the implementation in R (and sometimes additional materials) which they read themselves. In the class these topics are then jointly discussed and potentially unclear aspects elaborated further.
Supervised R coding: 
  • There will be some presentation of master solutions in R in the class, but subsequently students will work on some smaller exercises themselves (with teachers being available for help).
Homework: 
  • Students will receive exercises to deal with as homework.
Digital Resources
  • Reader
  • Practical exercises in R with commented solutions
  • Scientific papers on specific topics
Lesson Structure / Workload  
 Contact Hours 28
 Guided Self-Study 14
 Independent Self-Study 18
 Total Workload 60
Classroom Attendance Presence is strongly encouraged but not enforced with presence list.
Assessment Coursework (100%)
Language of Instruction  English
Comments -

 

Note

Course: Advanced Environmental Statistics
No.
n.BA.AD.AdES.24HS.V
Title
Advanced Environmental Statistics

Note

  • No module description is available in the system for the cut-off date of 07 November 2025.