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701-3001-00L 3 Credits BSC , MSC , DR D-USYS
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Environmental Systems Data Science: Data Processing

Lecturers & Examiners: Prof. Dr. Loïc Pellissier, Dr. Michele Volpi, Dr. Camille Pierre Albouy
VVZ CR n/a

Last Updated: 2026-06-01 11:30:27

Abstract

Students 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.

Objective

The 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

Content

● 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

General Information

Language
English
Levels
BSC , MSC , DR
Frequency
Yearly recurring

Examination

Type
ungraded semester performance
To pass the course, you need to complete at least 4 exercises (incl. peer-review!) with at least 60 points and complete the application.

Registration & Places

Max Places
80
Signup Start
27.08.2025
Priority: Registration for the course unit is until 19.09.2025 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Environmental Systems Data Science: Data Processing
  • Tue 08:15-09:00 (CHN C 14)
  • Tue 09:15-10:00 (CHN D 29)
  • Tue 09:15-10:00 (CHN D 44)
  • Tue 09:15-10:00 (CHN D 46)
  • Tue 09:15-10:00 (CHN F 46)
20 h semesterly

Offered In