The students will develop skills to conduct their own data management and analysis. They will be introduced to relevant concepts, terminology, and methods for handling data. At the end of the course, students will have a toolbox of scripts enabling them to optimize data management procedures by looping through data and using vector-oriented iterative processes. They will work in R studio, writing and debugging code for merging datasets, data cleaning and coding of different types of variables. They will be introduced to basic procedures for testing hypotheses. This includes tabulating basic statistical measures, specifying regression models, and interpreting and visualizing results. Throughout the course, the focus will be on making data handling process transparent and reflecting on the implications of data management and statistical approaches in relation to the validity and reliability of the results of the analysis.
Exam info and full course description can be found in the course catalogue.
Please notice that this course is passed by active participation, and you will not receive a specific grade. Only pass/fail will appear on your transcript.
Course specific:
To apply for the course, you must have passed a Bachelor's degree in Natural or Technical science. It is not a prerequisite that the students have used R, but it is an advantage. The course is designed so that both the novice and experienced users of R will feel challenged and learn new stuff.
General:
Exchange students: nomination from your home university
Freemovers: documentation for English Language proficiency
You can read more about admission here.
