More importantly, this course is composed of two main topics: data acquisition and data analysis.

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The main objectives of the data acquisition part are first to introduce students to the theoretical concepts and good practices to adopt when collecting scientific data. Among the topics discussed are: what is the utility of data collection, what are the available tools and resources for collecting data? How to perform a good testing session? What is there to do when a single case is only available? The second main objective is to provide students with the practical skills to design an experiment. To this end, they are taught how to program experiments on and how to write scripts with both the graphic user interface (builder) and the command line interface (coder) of this application.

After the students are taught how to acquire data, they learn how to analyse it (data analysis). This part of the course provides students the right tools and practices to perform analyses but has also been designed to help them gain awareness regarding which methodology and analyses to perform. Key statistics are therefore reviewed among which are descriptive statistics, inferential statistics and Bayesian statistics. As for the practical part, students learn to pre-process their data with R Studio (from raw data to tables of means) and then to perform the right analyses in R or JASP.

 

A flipped-classroom procedure is used for this course. Teaching programs are therefore adapted so that students first learn through repetition and practice by means of dedicated eBooks before the class and then attend a practical session which includes programming explanations and demonstrations.