ENVIRONMENTAL DATA ANALYSIS - PART 2
|Academic year||2020/2021 Syllabus of previous years|
|Official course title||ENVIRONMENTAL DATA ANALYSIS - MOD.2|
|Course code||CM0532 (AF:336439 AR:177262)|
|Modality||On campus classes|
|ECTS credits||6 out of 12 of ENVIRONMENTAL DATA ANALYSIS|
|Degree level||Master's Degree Programme (DM270)|
|Educational sector code||SECS-S/01|
|Spazio Moodle||Link allo spazio del corso|
Know how to gain information about such a dataset when presented with one.
Pre-process a dataset and prepare it for further analysis
Perform a variety of standard types of data analysis (e.g. correlations/regressions/EOFs)
Present project results in a clear and concise manner.
Inference for climate data
Time series components
Exploratory methods for time series
Statistical models for climate time series
von Storch, H. and Zwiers, F.W. (1999). Statistical Analysis in Climate Research. Cambridge University Press
Additional material (slides, notes) will be distributed by the teacher
1) preparation of an individual report regarding the analysis of an dataset.
The class project will entail choosing a problem (mutually agreed upon), writing code to solve it, and write a report which provides the background, motivation, solution method used and results. This project should ideally be a task you need to do for your thesis so that it is serves multiple purposes.
2) oral illustration of the report.
Grades will be determined by an oral exam (50%) and by a project (50%),
The final grade is an average of the grades reported in the individual modules
Theoretical lectures will be complemented by exercise classes and lab sessions. The statistical software used in the course is R (www.r-project.org).
Personal participation is important, and it is will help the student to learn more efficiently to read the assigned material to reinforce the lectures.
R scripts from various sources may be used to reinforce the material.