lant scientists use a wide range of experimental tools. Be it a simple weight measurement or an advanced imaging analysis, every experiment generates data that must be analyzed. Modern approaches for reproducible data analysis are mostly based on writing scripts in (statistical) programming languages. As such, working in the plant sciences and broader life sciences domain requires up-to-date data handling and programming skills.
In this course, students acquire practical skills for exploratory data analysis. Inspired by and working on real-life cases and datasets, students will develop a problem-solving mindset: from data acquisition to data cleaning, from statistical tests to reproducible and responsible reporting. All data analysis tasks will be performed in the R programming language, allowing the students to develop basic programming skills.
