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
Period
1st Semester
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
The course is one of the core educational activities of the Master's Degree Program in Environmental Sciences ( Curriculum in Global Environmental Change). It is the natural complement of the second module and aims at providing students with an introduction to statistical analysis of climate data. Important statistical methods including regression modeling, auto-correlation, smoothing and spectral analysis will be discussed and used to enhance data interpretation and answer scientific questions. Practicals with the R statistical software will be an integral part of the course.
Deal with major formats currently in use in environmental sciences and climate
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.
A basic understanding of Statistics (see for instance David S. Moore (2010) The Basic Practice of Statistics W. H. Freeman and Company). Programming of some form will be helpful.
Data storage methods (types of data formats and how best to deal with them)
Inference for climate data
Time series components
Exploratory methods for time series
Spectral analysis
Statistical models for climate time series
Chandler, R. and Scott, M. (2011). Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. Wiley
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
Final examination will consist of two steps:
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
This course is based on lectures, which will cover the major topics, emphasizing and discussing the important points.
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.

English
The attendance to each class (listening) and a genuine effort in doing the suggested exercises are two of the most important factors affecting the success in this course. Statistical analysis require both listening and doing.
oral
Definitive programme.
Last update of the programme: 20/04/2020