An HTML document was produced using RStudio software containing all the course material (text, R code, references and links, practical analyses, simulation exercises, etc.) in the form of an interactive e-book. This e-book was then made available to students on GitHub (a web service for hosting and managing software development) providing them with a common work environment dedicated to the course topic. This space, in addition to gathering the course resources, allows students to collaborate and exchange more easily among themselves and to discover and appropriate the use of the services offered by the platform widely used by data scientists (e.g., forum, R package development).
The weekly courses are divided into two parts, a course dedicated to the learning of concepts (more theoretical part) and a seminar dedicated to the supervision of the practical application of data analysis. In concrete terms, students come to class having already read the chapter of the week. This chapter is designed to expose them to the theoretical concepts, from the fundamental notions to the different methods of data analysis and their limits of application. Face-to-face time is used to review and expand on the material studied in advance, and to answer students' questions. The same approach is used in the seminar part where students validate their practical application of data analysis on concrete datasets, without disciplinary limits, using the open source statistical software R.
In order to reinforce the link between the learning of methodology and its application, in parallel to the classroom sessions, students, in groups of 2 to 3, carry out data analysis projects during the semester. The aim of this approach is to expose and train students to produce knowledge based on data, from the research question(s) that motivated the collection of data, to the choice of analysis methods, with a critical mind on the limits of the conclusions/decisions that can be drawn from the data. In concrete terms, they have a set of data and will carry out a complete analysis by testing different methods in order to provide one or more interpretations of the observed data and compare the methods used. They must also be able to argue their choice of analysis methods. In recognition of their effort, the best analyses are included in the e-book with acknowledgement to their authors.
The evaluation of the course is made up of a written report on the group project (focusing mainly on the reflective approach that led to the analysis presented) and an oral evaluation of the project and some of the fundamental notions seen in class.