Module

Environmental Data Science

Umweltdatenwissenschaft

6 LPWiSeEN

Voraussetzungen

Empfohlen, im Wortlaut

• Experience in R programming language • Basic knowledge of statistics (probability theory, hypothesis testing, correlation, linear model)

Inhalte

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

Lernziele

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.

Prüfungsleistung

• Exercises during course (10 small programming exercises; 6% each; 60% in total) • Oral presentation based on self-organized project: 15 min incl. discussion (40%)

Literatur (7)
  • 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/)

Bemerkungen

This module will be offered as a block course. Please bring your laptop.

Quelle: Modulbeschreibung der BTU · abgerufen 28.09.2026Original

Modulkatalog · inoffiziell

Betula ist ein inoffizielles Projekt und gehört nicht zur BTU.

Folia 1.0.3Radix 0.6.0

Daten vom 21.10.2026WiSe 2026/27Quelle: BTU