ENVIRONMENTAL DATA ANALYSIS

This course belongs to the educational activities of the PhD that allow the student to acquire instruments for data analysis. The objective of the course is to provide an introduction to R for the application of statistical methods for the environmental sciences.

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

1. Knowledge and understanding:
1.1 To know the most important statistical methods for data analysis, with focus on environmental data.

2. Ability to apply knowledge and understanding:
2.1 To know how to apply statistical methods.
2.2. To know how to apply autonomously the basic computational and programming tools of the R environment.

3. Ability to judge:
3.1 To be able to select the most suitable statistical methods for the problem at hand.

4. Communication skills:
4.1 To be able to communicate the results to the various stakeholders.
4.2 To be able to interact with the lecturer and the other students during the theoretical lessons and practical applications.

5. Learning skills:
5.1 To be able to take lecture notes to integrate and clarify the content of the referral teaching material.
5.2 To be able to self evaluate by addressing the lecturer’s questions and solving exercises.

Basic knowledge of statistics, mathematics, coding.

Basic R programming. Logical expressions. Vectors, matrices and data frames. Reading, writing, editing data. Conditional execution. Loops. How to speed up R code. Contributed packages.

Descriptive statistics. Statistical inference. Parametric and nonparametric hypothesis testing. Rank tests.

Analysis of tabular data.

Plotting.

Correlation and concordance.

Regression models. Estimation and hypothesis testing. Goodness of fit.

Time series: classic analysis. Trend and seasonality. ARIMA models. Forecasting.

Case studies.

Content may vary according to students’ background in statistics, mathematics, computer science.

Open source books on R
Scientific papers
Lecture notes given by the lecturer

Paper to be written according to a template given by the lecturer.

a) theoretical lessons describing the various concepts and methods
b) practicals with data analyses and result discussion and communication.

None.

written