Introduction to Programming for Statistics

Anno accademico

2022/2023 Programmi anni precedenti

Titolo corso in inglese

Introduction to Programming for Statistics

Codice insegnamento

PHD176 (AF:411432 AR:222420)

Lingua di insegnamento

Inglese

Modalità

In presenza

Crediti formativi universitari

3 su 6 di Introduction to Programming for Statistics and Machine Learning

Livello laurea

Master di Secondo Livello (DM270)

Settore scientifico disciplinare

SECS-S/01

Periodo

I Semestre

Anno corso

1

Sede

VENEZIA

Spazio Moodle

Link allo spazio del corso

Inquadramento dell'insegnamento nel percorso del corso di studio

Statistical analysis is a powerful tool in environmental studies. Using correct statistical methods and tools can help us to understand the data, as well as to infer potential causal relationship. The course presents statistical tools in R programming environment to analyse climate data with a focus on exploratory data analysis, geospatial analysis and regression approaches.

Risultati di apprendimento attesi

Students are expected to attain a basic understanding of R and RStudio, perform basic arithmetic and statistical operations in R. In addition, students are expected to understand basic file formats used in earth observations, and common approaches to read, process and analyse the data.

Prerequisiti

Some basic understanding of any programming language would be useful not required. Undergraduate level understanding of linear algebra and statistics would be useful.

Contenuti

Introduction to R / Rstudio, Basic Data structures (Vectors, Matrices, Data Frames), Arithmetic/Statistical operations in R, Plots & Handling data in R (Input/Output), Raster operations/NetCDF files in R, some recent packages for Summary Statistics, advanced geo-spatial operations

Testi di riferimento

In addition to the material provided in each lecture (which includes slides, data and scripts), additional information on below weblink are useful:

http://www.statmethods.net/ (Excellent to begin learning R)
https://cran.r-project.org/doc/contrib/ (Very useful resources)
https://cran.r-project.org/doc/manuals/R-intro.pdf (Quick Intro to R)
https://www.r-bloggers.com/tag/rwiki/ (Advance)

Modalità di verifica dell'apprendimento

During the course, the students will be asked to participate in interactive sessions (coding skills) and graded on their active engagement and a general understanding of programming concepts. These will count towards 100% for the final grade.

Modalità di esame

orale

Metodi didattici

Each lecture will combine a frontal lecture and in-class activities (hands-on sessions using sample data and analysis/scripts prepared in R). Activities will allow students to become familiar with the methods and tools introduced in the course for the analysis of environmental/geospatial data.

Altre informazioni

Further details about readings, required data and software installation including practical exercises will be communicated at the beginning of the course and published on Moodle.

Obiettivi Agenda 2030 per lo sviluppo sostenibile

Questo insegnamento tratta argomenti connessi alla macroarea "Cambiamento climatico e energia" e concorre alla realizzazione dei relativi obiettivi ONU dell'Agenda 2030 per lo Sviluppo Sostenibile

Programma definitivo.

Data ultima modifica programma: 09/05/2023