DATA ANALYSIS

The course is included in the Environmental Sciences master programme, curriculum on "Global Environmental Change". The main objective of the programme is to train highly skilled professionals, who could put into use their interdisciplinary knowledge for identifying and solving complex environmental problems. In particular, the curriculum aims at providing an integrated and systemic view of economic and environmental dynamics, in order to enable master graduates to deal with global environmental challenges, in the context of sustainable development. To this regard, the challenges connected to adaptation and mitigation to Climate Changes will be one of the major focus of the curriculum. In this context, this course provides an introduction to R for data analysis, including methods to solve differential equations for environmental problems.

Regular and active participation in the teaching activities offered by the course will enable students to:

1. (knowledge and understanding)
- be able to analyze an environmental data set using basic statistical methods;
- be able to describe a data set with tables and charts;
- to be able to do inference via statistical tests;
- to be able to apply linear regression;
- to be able to use the software R to solve differential equations for environmental problems.

2. (applying knowledge and understanding)
- be able to comment the results obtained using statistical and mathematical methods;
- be able to disseminate the results obtained using statistical and mathematical methods
- be able to put the results in the context of the other disciplines that form the basis for the study of the global environmental change.

3. (making judgements)
- make autonomous judgements about the results of data analysis;
- be able to communicate and disseminate the results of data analysis.

Basic concepts of mathematics and statistics.

1) Basic R programming.
Assignments, comparisons and logical expressions.
Conditional execution.
Loops.
How to speed up R code.

2) Data structures in R
Vectors, matrices and data frames.
Reading, writing, editing data.

3) Statistical methods in R
Contributed packages.
Basic statistics.
Computational statistics.
Hypothesis testing.
Linear regression for the joint study of two variables.
Charts and plotting.

4) Practical applications
Examples of programs for the analysis of ecological and environmental data.
ecological and environmental data analysis.
How to present the results.
How to comment the results.
How to disseminate the results.

5) Mathematical methods in R
Matrix eigenvalues and eigenvectors.
How to solve differential equations in R.

Open source books on R
Scientific papers
Lecture notes

Students will be assessed on the basis of a written test with open questions and exercises about the key points of the course. At least one simulated written test will be performed during the class.

Theoretical lectures
Exercise lectures
Practical lectures

English

None.

written