ENVIRONMENTAL DATA ANALYSIS - PART 1
|Academic year||2020/2021 Syllabus of previous years|
|Official course title||ENVIRONMENTAL DATA ANALYSIS - MOD.1|
|Course code||CM0532 (AF:336438 AR:177260)|
|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|
and knowing how to use it increases our understanding of environmental processes.
This module will provide an overview of commonly used statistical and graphical techniques for environmental data analysis.
Students will have the opportunity to design simple experiments, collect and analyse their own data, as well as analyse real data sets provided from different environmental research studies.
Moreover we give an introduction into R, a freely available statistical and computational environment, which is widely used by scientists all over the world.
No prerequisite programming experience is required.
In so doing, students will confront the complexity of real-world data, and learn and practice essential tools for capturing, manipulating and sharing data.
* Specific skills
1) Elementary knowledge of the programming language R and its application to the
1.1) data visualization
1.2) data modeling
2) Using Markdown languages to write a technical report
Elements of linear algebra and calculus in R
Introduction to environmental data analysis.
Distributions, sampling and descriptive stats
Figures, tables and data presentation
Regression and correlation
Analysis of variance
Piegorsch, W.W. and Bayler, A.J (2005) Analyzing Environmental Data, Wiley
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%),
This is the first module and every module will have its examination.
Theoretical lectures will be complemented by exercise classes and lab sessions. The statistical software used in the course is R (www.r-project.org).
The 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.