Academic year 2020/2021 Syllabus of previous years
Course code CM0532 (AF:336438 AR:177260)
Modality On campus classes
Degree level Master's Degree Programme (DM270)
Educational sector code SECS-S/01
Period 1st Semester
Course year 1
Spazio Moodle Link allo spazio del corso
Contribution of the course to the overall degree programme goals
The course is one of the core educational activities of the Master's Degree Program in Environmental Sciences ( Curriculum in Global Environmental Change). Quantitative data analysis is essential for Environmental Science
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.
Expected learning outcomes
In this course students will apply methods learned in a foundation course in Statistics to explore and answer key questions using relevant data from the Ecological and Environmental Sciences.
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
A basic understanding of Statistics (see for instance David S. Moore (2010) The Basic Practice of Statistics W. H. Freeman and Company).
R programming
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
Non-parametric statistics
Referral texts
Irizarry R. A. and Michael I Love, M.I (2015) Data Analysis for the Life Sciences, Leanpub
Piegorsch, W.W. and Bayler, A.J (2005) Analyzing Environmental Data, Wiley

Additional material (slides, notes) will be distributed by the teacher
Assessment methods
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%),

This is the first module and every module will have its examination.
Teaching methods
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).
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.

Teaching language
Further information
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.
Type of exam
Definitive programme.
Last update of the programme