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Making Sense of Data: Handling, Processing, Visualising, Spatial Mapping, Estimating, and Analysing Data in R

This course is open for applications from May 1 – May 31

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.

The course will cover the following topics:

  1. How to import and export data into and out of R
  2. How to work with different R objects and different types of variables
  3. How to implement operations and commands in R
  4. How to implement vector-based and non-vector-based loops
  5. How to create functions in R
  6. How to carry out statistical tests and model estimations
  7. How to visualize data in R using static, dynamic, and interactive plots
  8. How to report data effectively and efficiently
  9. How to formulate research hypotheses and design identifications strategies  

Exam info and full course description

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.

Admission Requirements

Course specific:

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.


Exchange students: nomination from your home university

Freemovers: documentation for English Language proficiency

You can read more about admission here.