ENVIRONMENTAL DATA ANALYSIS

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 analyze their own data, as well as analyze 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 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.
Exploratory analysis
Distributions, sampling.
Estimation methods.
Hypothesis testing
Regression and correlation
Analysis of variance
Non-parametric statistics
Descriptive techniques for time series analysis
Spectral analysis

Chandler, R. and Scott, M. (2011). Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. Wiley
Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical Methods in Water Resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 458 p. (https://doi.org/10.3133/tm4A3 )
Dormann, C. (2019) Environmental Data Analysis, Springer
Qian, S.S. (2016) Environmental and Ecological Statistics with R, (2nd ed), CRC press

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

The exam consist of a test directly on the computer, in which each student will analyze a data set proposed by the lecturer, and he/she will also have to answer some questions on the methodological part.
It is a closed book exam.

Student will be evaluated in terms of
- quality of his/her statistical analyses (max 11 points)
- correct use of the technical terminology (max 11 points)
- correct conclusions (max 11 points)

To encourage active participation in class, 6 15-minute quizzes will be given every two weeks during class time. Completing the 6 tests can earn up to 3 points. An overall mark of 33 or above will be awarded with honours.

written

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

| **Grade** | **Equivalent in %** | **Description** | **Ranking** |
|-----------|---------------------|-----------------------|------------------------|
| 30L (30 e lode) | 97-100% | With honors (cum laude) | Excellent (Eccellente) |
| 30 | 93-96% | Excellent | Eccellente |
| 29 | 90-92% | Very good | Ottimo |
| 28 | 87-89% | Very good | Ottimo |
| 27 | 83-86% | Good | Buono |
| 26 | 80-82% | Good | Buono |
| 25 | 76-79% | Satisfactory | Discreto |
| 24 | 73-75% | Satisfactory | Discreto |
| 23 | 70-72% | Sufficient | Sufficiente |
| 22 | 66-69% | Sufficient | Sufficiente |
| 21 | 63-65% | Pass | Sufficiente |
| 20 | 60-62% | Pass | Sufficiente |
| 19 | 55-59% | Barely passing | Sufficiente |
| 18 | 50-54% | Minimum passing | Sufficiente |
| <18 | Below 50% | Fail | Insufficiente |

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 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.

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.
Access to Moodle for the 2026-2027 academic year will be granted to students who have the course in their study plan.
Request to gaetan@unive.it, join your name and surname student ID number degree program a screenshot of your study plan.