Vorlesung/Übung
4 SemesterwochenstundenBTU-Vorlesungsverzeichnis ↗- —25 Jan 2027 bis 29 Jan 202709:00 bis 17:00LG 4 A, Raum 3.22
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13300Fakultät 2 – Umwelt und Naturwissenschaften
Umweltdatenwissenschaft
Titel auf Englisch: Environmental Data Science
Lernergebnisse
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.
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
Voraussetzungen
Verpflichtende Voraussetzungen
Keine
Empfohlene Voraussetzungen
- Experience in R programming language
- Basic knowledge of statistics (probability theory, hypothesis testing, correlation, linear model)
Prüfungsmodalitäten
- Exercises during course (10 small programming exercises; 6% each; 60% in total)
- Oral presentation based on self-organized project: 15 min incl. discussion (40%)
Termine im WiSe 26/27
Angeboten in
- Environmental and Resource ManagementMaster
Wahlpflichtkomplex: Kein Komplex genannt
| Studiengang | Abschluss | Wahlpflichtkomplex |
|---|---|---|
| Environmental and Resource Management | Master | Kein Komplex genannt |
Literatur und Materialien
- 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/)
Hinweise
This module will be offered as a block course. Please bring your laptop
Bewertungen von Studierenden
Was Studierende über dieses Modul sagen. Die BTU hat damit nichts zu tun — hier schreiben nur Studierende, und jede Bewertung ist die Meinung einer einzelnen Person.