Individual longitudinal or sequence data are common to many fields. For instance, they are essential for understanding and predicting the evolution of a patient's disease after it has been diagnosed (survival analysis), the behavior of a visitor of a web site (web log mining), but also for categorizing or clustering signal sequences in domains such as telecommunication. This paper focuses on the analysis of individual longitudinal data within social sciences, especially in population science where we are interested in describing and understanding life courses. A life event can be seen as the change of state of some discrete variable, e.g. the marital status, the number of children, the job, the place of residence. Such life history data are collected in mainly two ways: As a collection of time stamped events or as state sequences. The former is used for instance by survival analysis that focuses on a given type of event and is concerned with its hazard rate or equivalently the duration until it happens. Sequence analysis on the other hand is concerned with the sequencing of the events and is best suited for characterizing whole life trajectories. We consider using datamining-based approaches borrowed from other fields for analysing life courses with both a survival and a sequence perspective. We put stress on the social scientist's expectations and address some of the statistical challenges they raise.