701-3001-00L  Environmental Systems Data Science: Data Processing

SemesterHerbstsemester 2022
DozierendeL. Pellissier, E. J. Harris, J. Payne, M. Volpi
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
Kommentar**Students who have taken 701-3001-00L Environmental Systems Data Science in autumn semester 2020 or 21 are not allowed to take this lecture. The content is similar.**

Number of participants is limited to 80.

Course registration starts on 31.08.2022.
Priority is given to the target groups until 23.09.2022,

Target groups
Agricultural Sciences MSc
Environmental Sciences MSc
Atmospheric and Climate Science MSc
Environmental Sciences PhD
Agricultural Sciences PhD

Waiting list will be deleted on 30.09.2022


KurzbeschreibungStudents are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses.
LernzielThe students are able to
● frame a data science problem and build a hypothesis
● describe the steps of a typical data science project workflow
● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models
● critically think about the limits and implications of a method
● visualise data and results throughout the workflow
● access online resources to keep up with the latest data science methodology and deepen their understanding
Inhalt● The data science workflow
● Access and handle (large) datasets
● Prepare and clean data
● Analysis: data exploratory steps
● Analysis: machine learning and computational methods
● Evaluate results and analyse uncertainty
● Visualisation and communication
Voraussetzungen / Besonderes252-0840-02L Anwendungsnahes Programmieren mit Python
401-0624-00L Mathematik IV: Statistik
401-6215-00L Using R for Data Analysis and Graphics (Part I)
401-6217-00L Using R for Data Analysis and Graphics (Part II)
701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften