Data Analysis in R

Note: all our courses are taught online this year.

This course is aimed at teaching the students the basics of statistical programming in R. While doing so, the students will learn to understand statistical models and analysing/interpreting the results given by the statistical software. 

Course days11-15 January 2021
Course levelMaster, PhD candidates and professionals from all disciplines 
Coordinating lecturerA. Bassi
Other lecturersTba
Forms of tuitionInteractive seminar
Forms of assessmentWritten seminar
Credits3 ECTS
Contact hours30 hours
Tuition feeRead all information about our tuition fees and what's included here  
How to applyFind our application form here 
Students and professionals with a basic knowledge of statistics (at least an undergraduate course is a pre-requisite) who are interested in learning the basics and some more advanced skills of R, and applying them to solving their data analysis problems. If you have doubts about your eligibility for the course, please contact us: [email protected]

Highly recommended: Basic statistics knowledge and knowledge of basic programming concepts.

We start with the data structures present in R (vectors, matrices, lists, data frames) and how to perform simple operations with them. Then we learn how to import the data in R and how to save the output of your analyses.

The next step would be to learn logical operations and key elements to navigate through datasets. Once the students have familiarised themselves with these basic concepts we then move to more advanced programming concepts such as understanding and writing of functions, string operations, and list operations.

Besides these technical skills, the students will learn how to compute descriptive statistics and how to produce a visual representation of data in R. Then, the linear regression model is introduced, a widely used model with two main purposes: modeling relationships among the data and predicting future observations. After that, the linear model will be extended to the generalised linear framework, in order to analyse non-normally distributed variables. All the statistical theory will be briefly covered first and then applied examples using R will be presented, such that the students can learn the methods by applying them.

Every day consists of short lectures with examples, and exercises in which the students can apply what they have learned right away. At the end of the course, there will be an assignment that will be graded. The focus in the exercises and assignment is the coding in R and how to apply and interpret generalised linear regression models. By the end of the course, the students will be acquainted with various popular R packages, can write their own functions, and use attractive plots to present their data. 


Upon successful completion of the course, students will have developed their programming skills in R and will be able to statistically evaluate quantitative data sources, conduct various statistical tests, and analyse data using generalised linear frameworks.