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

Final examination will consist of two steps:
1) preparation of a report regarding the analysis of a dataset.
The report 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.
2) oral illustration of the report.


During the oral examination, questions may be asked about parts of the program not covered in the report.
Grades will be determined by an oral exam (50%) and by a project (50%).

Student will be evaluated in terms of
- quality of his/her statistical analyses
- correct use of the technical terminology
- correct conclusions
- quality of presentation (report)
- quality of the oral discussion

Rules:
1) if the student fails the exam, they can try another session with the *same* project. However, if the exam is failed again, then a *new* project must be considered for the subsequent exam sessions
2) if the student passes the exam but decides to decline the score, then a *new* project must be considered for the subsequent exam sessions

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.

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