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

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