This module has two main aims: introduce to secondary data acquisition, management and analysis in the social sciences; prepare to attend further statistical training and make use of statistics in future research works, academic (such as master's or PhD dissertations) or not. It consists of nine weeks of teaching. Seminars are designed as discussions on theory and concepts, as well as ‘hands-on’ computer workshops, giving you direct experience of exploring and analysing data in the R environment. R is a free and open software that you easily download and install on your computer.

The module can help you work with different kinds of data, such as individual surveys, country-level aggregate data, corporate statistics, administrative records and data collected online. However, for practical reasons, in the module, we will focus on a selection of individual-level survey datasets, relevant to the social sciences at large. Skills acquired with such surveys may easily be transposed to other data.

This module does not require any prior knowledge of mathematics or statistics. You only need to understand the importance of statistics for empirical social sciences, and show willingness to learn about them. The focus will be less on mathematical equations than on the selection and application of a range of methods and tools to concrete social scientific data. You will be invited to use in this module skills that you have been developing in other domains, that is, logical and causal reasoning, research design, reading and writing. Statistical reasoning and computer languages are best acquired on the basis of a good command of natural language (in this case, English).

If you already have some knowledge or practice of statistics, please be aware that this module is designed for a range of levels, including beginners, as you may skim or skip the parts that you are confident with, and spend more time on more advanced readings and weekly “Going further” exercises. On the contrary, if you are a beginner, you may rather focus on understanding the lecture, reading on the aspect of the module where you lack background (either statistics, coding, or social scientific reasoning), and doing the “Core” exercises.