Week 1
First, we will practice using essential Tidyverse tools that enable powerful data wrangling and visualization within an efficient and reproducible workflow. Our focus will be on plotting, transforming, and joining data that is already in a tidy format.
Week 2
Next, we will reshape untidy data into a tidy format, practice additional programming tools, begin training algorithms, and focus on creating reproducible reports.
Week 3
After having practiced the essentials of a data science workflow (data wrangling and modelling), we will now shift our focus to a common form of ecological data: time series data with repeated measurements on individual plants or animals. We will thus engage in time series analyses to expand our skills further.
Week 4
This week we will shift focus to another typical form of ecological data and analyses: the use of image-based data and analyses as used for e.g. wildlife monitoring, phenological studies or agronomic surveyance. We will thus focus on the use of such data in (agro-)ecology and begin conducting image analyses.
Week 5 & 6
During the last part of the course, we will work in small groups on a selected data science challenge. Our main goal is to apply all the theoretical knowledge gained and skills practiced, and thus to generate value from data. Since data science is mostly a collaborative effort, we’ll be working in small teams.
17/4 2025
At the final day of the course, each team will present the value they generated in their data science challenge.