13300 - Environmental Data Science Modulübersicht

Module Number: 13300
Module Title:Environmental Data Science
  Umweltdatenwissenschaft
Department: Faculty 2 - Environment and Natural Sciences
Responsible Staff Member:
  • Prof. Dr. Ryo, Masahiro
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Every winter semester
Credits: 6
Learning Outcome:After the course, students will acquire both hard and soft data science skills essential for solving issues in environmental science, such as data handling, data visualization, data analysis, data interpretation, and data communication. These practical skills will be supported by fundamental statistical and programming knowledge. The students will be fluent in the R programming language beyond the basic level. 
Contents:Part 1. Introduction to environmental data science
Part 2. Data handling
  - Coding Basics in R, data import & data transformation
Part 3. Data visualization
  - Various types of data visualization
Part 4. Data analysis
  - Basic statistics
  - Statistical modelling: classification and regression
  - Machine learning modelling: Classification and regression
Part 5. Data interpretation
  - Model interpretation
  - Common pitfalls
Part 6. Data communication
  - Storytelling with data: Basic rules & practical tips
Recommended Prerequisites:
  • Experience in R programming language
  • Basic knowledge of statistics (probability theory, hypothesis testing, correlation, linear model)
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Exercise / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:Most of the textbooks are freely available from the links thanks to the fantastic authors:
  • H. Wickham & G. Grolemund: R for Data Science (https://r4ds.had.co.nz/index.html)
  • R. A. Irizarry: Introduction to Data Science (https://rafalab.github.io/dsbook/)
  • Hands-On Programming with R (https://rstudio-education.github.io/hopr/index.html)
  • C.O.Wilke: Fundamentals of Data Visualization (https://clauswilke.com/dataviz/)
  • B. Boehmke & B. Greenwell: Hands-On Machine Learning with R (https://bradleyboehmke.github.io/HOML/)
  • C. Molnar: Interpretable Machine Learning (https://christophm.github.io/interpretable-ml-book/)
  • C. N. Knaflic: Storytelling with Data (https://www.storytellingwithdata.com/)
Module Examination:Continuous Assessment (MCA)
Assessment Mode for Module Examination:
  • Exercises during course (10 small programming exercises; 6% each; 60% in total)
  • Oral presentation based on self-organized project: 15 min incl. discussion (40%)
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:15
Part of the Study Programme:
  • Master (research-oriented) / Environmental and Resource Management / PO 2011
  • Master (research-oriented) - Double Degree / Environmental and Resource Management / PO 2021
  • Master (research-oriented) / Environmental and Resource Management / PO 2021
Remarks:This module will be offered as a block course. Please bring your laptop.
Module Components:242100 Lecture/Exercise: Environmental Data Science

Components to be offered in the Current Semester:
  • no assignment