Learning goals
The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the data available.

Knowledge and understanding
The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data. Also an introductory knowledge of extreme value estimation and modeling will be part of his cultural heritage

Applying knowledge and understanding
Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed.

Making judgements
Through the homeworks and the final presentations discussions, tudente will develop judgements capacity in terms of theoretical choices in representation of real worls phenomena.

Communication skills
Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed.
This procedure will help the student to develop his/her ability to communicate the results of its work.

Learning skills
One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.